The Influence of Carbonate Rocks Reservoir Parameters on Microscopic Flow
-
摘要: 针对碳酸盐岩油藏孔隙大小的双峰分布特征，首先利用计算机模拟建立了描述不同孔隙特征的大孔隙网络模型和微孔隙网络模型，在此基础上提出一种耦合算法构建出的同时描述大孔隙和微孔隙特征的碳酸盐岩双孔隙网络模型；然后，基于侵入-逾渗理论，模拟双孔隙网络模型中油水两相流体的一次驱替和二次吸吮过程，并建立了毛细管压力和相对渗透率的求解模型；最后，通过调整双孔隙网络结构参数，模拟水湿油藏条件下碳酸盐岩储层参数对相对渗透率曲线的影响. 结果表明，随着微孔隙比例因子和平均配位数的增加，油相相对渗透率曲线升高；随着双孔隙半径比的增加，油相和水相相对渗透率曲线下降，这对碳酸盐岩油藏渗流机理研究有着重要的指导意义.Abstract: Due to bimodal pore size distribution of carbonate rocks, macro pore network and micro pore network are produced to describe different pore characteristics separately, and carbonate dual pore network is constructed with the coupling of macro pore and micro pore network. Then primary drainage and secondary imbibition process are simulated based on intrusion-percolation theory during the oil-water two phase fluid flow, the capillary pressure and relative permeability model are calculated. At last, the influence of reservoir parameters on relative permeability curve is studied under water-wet conditions by adjusting the network structure parameters. Results show that oil relative permeability curve rises with the increase of micro pore density factor and average coordination number, and oil and water relative permeability curve drops with the increase of dual pore radius ratio. These phenomena are important to the study of carbonate reservoir microscopic flow mechanism.
-
图 2 碳酸盐岩双孔隙网络模型的构建
a.随机大孔隙网络；b.随机微孔隙网络；c.双孔隙网络
Fig. 2. Construction of carbonate dual pore scale network model
图 3 不同微孔隙比例因子下的相对渗透率曲线
Fig. 3. Relative permeability curves in different micro pore density factors
图 4 不同配位数下的相对渗透率曲线
Fig. 4. Relative permeability curves in different coordination numbers
图 5 不同双孔隙半径比下的相对渗透率曲线
Fig. 5. Relative permeability curves in different dual pore radius ratios
表 1 网络模型的基本参数
Table 1. Basic parameters of network models
基本参数 大孔隙网络 微孔隙网络 双孔隙网络 网络尺寸 0.1 mm×0.1 mm×0.1 mm 0.1 mm×0.1 mm×0.1 mm 0.1 mm×0.1 mm×0.1 mm 孔隙数目 125 8 000 8 072 喉道数目 139 16 376 17 453 平均配位数 3.15 3.65 4.02 网络孔隙度 10.37% 17.21% 25.46% 
下载: 导出CSV
-
[1] Biswal, B., Oren, P.E., Held, R.J., et al., 2007. Stochastic Multiscale Model for Carbonate Rocks. Physical Review E Stat. Nonlin Soft Matter Phys. , 75(6-1): 061303 http://www.ncbi.nlm.nih.gov/pubmed/17677251 [2] Biswal, B., Øren, P.E., Held, R.J., et al., 2009. Modeling of Multiscale Porous Media. Image Analysis and Stereology, 28(1): 23-34. doi: 10.5566/ias.v28.p23-34 [3] Fatt, I., 1956a. The Network Model of Porous Media Ⅰ. Capillary Pressure Characteristics. Petroleum Transactions, AIME, 207: 144-159. doi: 10.2118/574-G [4] Fatt, I., 1956b. The Network Model of Porous Media Ⅱ. Dynamic Properties of a Single Size Tube Network. Petroleum Transactions, AIME, 207: 160-163. [5] Fatt, I., 1956c. The Network Model of Porous Media Ⅲ. Dynamic Properties of Networks with Tube Radius Distribution. Petroleum Transactions, AIME, 207: 164-181. http://www.researchgate.net/publication/285513534_The_network_model_of_porous_media_III_Dynamic_properties_of_networks_with_tube_radius_distribution [6] Gao, H.M., Jiang, H.Q., Chen, M.F., 2007. Simulation Study on the Effect of the Microscopic Parameters of Reservoir Pore Structure on Oil-Water Relative Permeability. Journal of Xi'an Shiyou University(Natural Science Edition), 22(2): 56-59 (in Chinese with English abstract). [7] Hou, J., Li, Z.Q., Guan, J.T., et al., 2005. Water Flooding Microscopic Seepage Mechanism Research Based on Three-Dimension Network Model. Chinese Journal of Theoretical and Applied Mechanics, 37(6): 783-787 (in Chinese with English abstract). [8] Jiang, Z., Van Dijke, M.I.J., Wu, K., et al., 2011. Stochastic Pore Network Generation from 3D Rock Images. Transport in Porous Media, 94(2): 571-593. doi: 10.1007/s11242-011-9792-z [9] Knackstedt, M., Sheppard, A., Arns, C., et al., 2006.3D Imaging and Characterization of the Pore Space of Carbonate Core; Implications to Single and Two Phase Flow Properties. SPWLA 47th Annual Logging Symposium, Veracruz, Mexico. [10] Knackstedt, M.A., Arns, C.H., Sok, R.M., et al., 2007.3D Pore Scale Characterisation of Carbonate Core: Relating Pore Types and Interconnectivity to Petrophysical and Multiphase Flow Properties. International Petroleum Technology Conference, Dubai, United arab emirates, Society of Petroleum Engineers. [11] Li, Z.Q., Hou, J., Cao, C.L., et al., 2005. Microscopic Simulation for Influence of Microscopic Reservoir Parameters on Remaining Oil Distribution. Acta Petrolei Sinica, 26(6): 69-73 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-SYXB200506014.htm [12] Tao, J., Yao, J., Fan, Z.F., et al, 2008. A New Method for Determination of Start-Up Pressure Gradient in Low Permeability Reservoir. Xinjiang Petroleum Geology, 29(5): 626-628 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-XJSD200805033.htm [13] Valvatne, P.H., Blunt, M.J., 2004. Predictive Pore-Scale Modeling of Two-Phase Flow in Mixed Wet Media. Water Resources Research, 40(7): W07406. doi: 10.1029/2003WR002627 [14] Vogel, H.J., Roth, K., 2001. Quantitative Morphology and Network Representation of Soil Pore Structure. Advances in Water Resources, 24(3-4): 233. doi: 10.1016/S0309-1708(00)00055-5 [15] Wang, K.W., Sun, J.M., Guan, J.T., et al., 2008. Network Model Modeling of Microcosmic Remaining Oil Distribution after Polymer Flooding. Journal of China University of Petroleum, 30(1): 72-76 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTOTAL-SYDX200601015.htm [16] Wu, K.J., Dijke, M.I.J., Couples, G., et al., 2006.3D Stochastic Modelling of Heterogeneous Porous Media-Applications to Reservoir Rocks. Transport in Porous Media, 65(3): 443-467. doi: 10.1007/s11242-006-0006-z [17] Wu, K.J., Jiang, Z.Y., Couples, G.D., et al., 2007. Reconstruction of Multi-Scale Heterogeneous Porous Media and Their Flow Prediction. International Symposium of the Society of Core Analysts, Calgary, Canada. [18] Wu, K.J., Jiang, Z.Y., Ma, J.S., et al., 2011. Multiscale Pore System Reconstruction and Integration. International Symposium of the Society of Core Analysts, Austin, Texas, USA. [19] Yao, J., Tao, J., Li, A.F., 2007. Research on Oil-Water Two-Phase Flow Using 3D Random Network Model. Acta Petrolei Sinica, 28(2): 94-97 (in Chinese with English abstract). http://www.researchgate.net/publication/289047925_Research_on_oil-water_two-phase_flow_using_3D_random_network_model [20] 高慧梅, 姜汉桥, 陈民锋, 2007. 储层孔隙结构对油水两相相对渗透率影响微观模拟研究. 西安石油大学学报: 自然科学版, 22(2): 56-59. doi: 10.3969/j.issn.1673-064X.2007.02.015 [21] 侯健, 李振泉, 关继腾, 等, 2005. 基于三维网络模型的水驱油微观渗流机理研究. 力学学报, 37(6): 783-787. doi: 10.3321/j.issn:0459-1879.2005.06.016 [22] 李振泉, 侯健, 曹绪龙, 等, 2005. 储层微观参数对剩余油分布影响的微观模拟研究. 石油学报, 26(6): 69-73. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXB200506014.htm [23] 陶军, 姚军, 范子菲, 等, 2008. 一种确定低渗透油藏启动压力梯度的新方法. 新疆石油地质, 29(5): 626-628. https://www.cnki.com.cn/Article/CJFDTOTAL-XJSD200805033.htm [24] 王克文, 孙建孟, 关继腾, 等, 2006. 聚合物驱后微观剩余油分布的网络模型模拟. 中国石油大学学报: 自然科学版, 30(1): 72-76. doi: 10.3321/j.issn:1000-5870.2006.01.015 [25] 姚军, 陶军, 李爱芬, 2007. 利用三维随机网络模型研究油水两相流动. 石油学报, 28(2): 94-97. doi: 10.3321/j.issn:0253-2697.2007.02.018 -
点击查看大图
图(5) / 表(1)
计量
- 文章访问数: 2832
- HTML全文浏览量: 164
- PDF下载量: 347
- 被引次数: 0
