S-Wave Velocity Prediction Method of Volcanic Rock Based on Statistical Rock-Physics Model
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摘要: 火山岩储层因蕴藏地热、矿产、油气等资源，受到各国学者的广泛关注. 横波速度是地震AVO分析、储层特征描述和流体识别的必要信息，因受限于采集技术和采集成本等因素，测井资料中常常缺失横波速度信息. 基于统计学岩石物理反演方法和Xu-White岩石物理模型，提出了一种适用于火山岩储层的统计学岩石物理模型横波速度预测方法. 该方法利用Xu-White模型中砂的纵、横波速度和粘土相关孔隙纵横比作为关键岩石物理参数对岩石速度的影响，根据统计学岩石物理反演方法，首先通过已知参考井常规测井资料和Xu-White模型构建关键岩石物理参数的先验分布，其次利用先验分布代替固定参数值初始化Xu-White模型建立统计学岩石物理模型，再次基于贝叶斯反演理论，匹配目标井位实际纵波速度与模拟纵波速度，计算目标井位关键岩石物理参数后验信息，最后利用统计学岩石物理模型和目标井关键岩石物理参数的后验分布反演出目标井的横波速度信息. 将该方法应用于中国东部南堡凹陷5号构造实际测井数据，得到的横波速度预测结果优于常规方法，证明了该方法的有效性和准确性. 研究将为火山岩储层的后续勘探开发提供更准确的横波速度资料.Abstract: Because of its geothermal, mineral, and oil and gas resources, volcanic rock reservoirs have piqued the interest of scholars from all over the world. Shear-wave velocity is crucial for seismic AVO analysis, reservoir characterization, and fluid identification. Shear wave velocity information is often missing in logging data due to limitations in acquisition technology and acquisition cost.Based on the statistical petrophysical inversion method and Xu-White model, this paper proposes a S-wave velocity prediction method of statistical rock-physics model suitable for volcanic reservoirs. This method uses the P-wave and S-wave velocity of sand and the aspect ratio of clay-related pores in the Xu-White model as the key petrophysical parameters affecting rock velocity. Firstly, according to the statistical rock physical inversion method, the prior distribution of key petrophysical parameters is constructed by the conventional logging data of reference wells and the Xu-White model. Secondly, the prior distribution is used to initialize the Xu-White model instead of the fixed parameter value to establish the statistical rock-physics model. Thirdly, based on the Bayesian inversion theory, the actual P-wave velocity and the simulated P-wave velocity of the target well are matched to calculate the posterior information of key petrophysical parameters of the target well. Finally, the S-wave velocity information of the target well is inversed by using the statistical rock-physics model and the posterior distribution of key petrophysical parameters of the target well. This method is applied to the actual logging data of No. 5 structure in Nanpu Sag, eastern China, and the S-wave velocity prediction results are better than those of the conventional method, which proves the effectiveness and accuracy of this method. This study will provide more accurate S-wave velocity for the subsequent exploration and development of volcanic reservoirs.
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图 1 Xu-White模型中不同砂的速度下模拟出的岩石纵、横波速度
Fig. 1. The simulated P-wave and S-wave velocities of rock with different velocities of sand in Xu-White model
图 2 Xu-White模型中不同砂相关孔隙纵横比下模拟出的岩石纵、横波速度
Fig. 2. The simulated P-wave and S-wave velocities with different aspect ratios of sand-related pores in Xu-White model
图 3 Xu-White模型中不同粘土相关孔隙纵横比下模拟出的岩石纵、横波速度
Fig. 3. The simulated P-wave and S-wave velocities with different aspect ratios of clay-related pores in Xu-White model
图 4 基于统计学岩石物理模型的横波速度预测工作流程
Fig. 4. Workflow of S-wave velocity prediction based on statistical rock-physics model
图 5 参考井的测井曲线和矿物组分信息
Fig. 5. Logging curves and mineral composition information of target well
图 7 参考井关键岩石物理参数的统计学先验信息
Fig. 7. Statistical prior information of key petrophysical parameters of reference wells
图 8 目标井关键岩石物理参数的统计学后验信息
Fig. 8. Statistical posterior information of key petrophysical parameters of target well
图 9 基于3种方法纵、横波速度预测结果
Fig. 9. Prediction results of P-wave and S-wave velocities based on three methods
表 1 矿物和孔隙流体的模量和密度
Table 1. Modulus and density of minerals and fluids
体积模量(Bulk modulus)(GPa) 剪切模量(Shear modulus)(GPa) 密度(Density)(g/cm3) 石英(Quartz) 37.9 44.3 2.65 岩浆包裹体(SMI) 37.5 15 2.62 粘土(Clay) 25 9 2.55 气体(Gas) 0.336 – 0.34 水(Water) 2.2 – 1.40 
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表 2 不同方法预测的$ {\mathit{V}}_{\mathrm{p}} $和$ {\mathit{V}}_{\mathrm{s}} $的均方误差(MSE)和相关系数($ \mathit{r} $)
Table 2. Mean square error (MSE) and correlation coefficient (r) of P-save and S-wave velocity predicted by different methods
方法 MSE of Vp MSE of Vs r of Vp r of Vs Han经验公式 0.112 94 0.143 14 0.801 31 0.753 2 常规Xu-White模型 0.054 99 0.070 57 0.858 21 0.824 7 统计学岩石物理模型 0.022 41 0.030 12 0.967 13 0.956 9
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[1] Bachrach, R., 2006. Joint Estimation of Porosity and Saturation Using Stochastic Rock-Physics Modeling. Geophysics, 71(5): O53-O63. https://doi.org/10.1190/1.2235991 [2] Bai, J. Y., Song, Z. X., Su, L., et al., 2012. Error Analysis of Shear-Velocity Prediction by the Xu-White Model. Chinese Journal of Geophysics, 55(2): 589-595(in Chinese with English abstract). [3] Berryman, J. G., 1980. Long-Wavelength Propagation in Composite Elastic Media II. Ellipsoidal Inclusions. The Journal of the Acoustical Society of America, 68(6): 1820-1831. https://doi.org/10.1121/1.385172 [4] Castagna, J. P., Batzle, M. L., Eastwood, R. L., 1985. Relationships between Compressional-Wave and Shear-Wave Velocities in Clastic Silicate Rocks. GEOPHYSICS, 50(4): 571-581. https://doi.org/10.1190/1.1441933 [5] Gassmann, F., 1951. Elastic Waves through a Packing of Spheres. Geophysics, 16(4): 673-685. https://doi.org/10.1190/1.1437718 [6] Grana, D., Della Rossa, E., 2010. Probabilistic Petrophysical-Properties Estimation Integrating Statistical Rock Physics with Seismic Inversion. GEOPHYSICS, 75(3): O21-O37. https://doi.org/10.1190/1.3386676 [7] Greenberg, M. L., Castagna, J. P., 1992. Shear-Wave Velocity Estimation in Porous Rocks: Theoretical Formulation, Preliminary Verification and Applications. Geophysical Prospecting, 40(2): 195-209. https://doi.org/10.1111/j.1365-2478.1992.tb00371.x [8] Han, D. H., Nur, A., Morgan, D., 1986. Effects of Porosity and Clay Content on Wave Velocities in Sandstones. Geophysics, 51(11): 2093-2107. https://doi.org/10.1190/1.1442062 [9] Hawlander, H. M., 1990. Diagenesis and Reservoir Potential of Volcanogenic Sandstones-Cretaceous of the Surat Basin, Australia. Sedimentary Geology, 66(3/4): 181-195. https://doi.org/10.1016/0037-0738(90)90059-3 [10] Hill, R., 1952. The Elastic Behaviour of a Crystalline Aggregate. Proceedings of the Physical Society Section A, 65(5): 349-354. https://doi.org/10.1088/0370-1298/65/5/307 [11] Hunter, B. E., Davies, D. K., 1979. Distribution of Volcanic Sediments in the Golf Coastal Province-Significance to Petroleum Geology. Transactions, Golf Coast Association of Geological Societies, 29(1): 147-155. [12] Keys, R. G., Xu, S. Y., 2002. An Approximation for the Xu-White Velocity Model. Geophysics, 67(5): 1406-1414. https://doi.org/10.1190/1.1512786 [13] Liu, C., Qiao, H. Q., Guo, Z. Q., et al., 2017. Shale Pore Structure Inversion and Shear Wave Velocity Prediction Based on Particle Swarm Optimization (pso) Algorithm. Progress in Geophysics, 32(2): 689-695(in Chinese with English abstract). [14] Mollajan, A., Memarian, H., Quintal, B., 2019. Imperialist Competitive Algorithm Optimization Method for Nonlinear Amplitude Variation with Angle Inversion. Geophysics, 84(3): N81-N92. https://doi.org/10.1190/geo2018-0507.1 [15] Pan, B. Z., Xue, L. F., Huang, B. Z., et al., 2008. Evaluation of Volcanic Reservoirs with the "QAPM Mineral Model" Using a Genetic Algorithm. Applied Geophysics, 5(1): 1-8. https://doi.org/10.1007/s11770-008-0004-8 [16] Pillar, N., Yan, J., Lubbe, R., 2007. Variable Aspect Ratio Method in the Xu-White Model for AVO. In 69th EAGE Conference and Exhibition Incorporating SPE EUROPEC 2007. European Association of Geoscientists & Engineers. cp-27-00230. https://doi.org/10.3997/2214-4609.201401648 [17] Qiao, H, Q., 2017. Shear Wave Velocity Prediction Method Based on Improved Particle Swarm Algorithm(Dissertation). Jilin University, Changchun (in Chinese with English abstract). [18] Rajabi, M., Bohloli, B., Gholampour Ahangar, E., 2010. Intelligent Approaches for Prediction of Compressional, Shear and Stoneley Wave Velocities from Conventional Well Log Data: a Case Study from the Sarvak Carbonate Reservoir in the Abadan Plain (Southwestern Iran). Computers & Geosciences, 36(5): 647-664. https://doi.org/10.1016/j.cageo.2009.09.008 [19] Ruiz, F., Dvorkin, J., 2010. Predicting Elasticity in Nonclastic Rocks with a Differential Effective Medium Model. Geophysics, 75(1): E41-E53. https://doi.org/10.1190/1.3267854 [20] Sohail, G. M., Hawkes, C. D., 2020. An Evaluation of Empirical and Rock Physics Models to Estimate Shear Wave Velocity in a Potential Shale Gas Reservoir Using Wireline Logs. Journal of Petroleum Science and Engineering, 185: 106666. https://doi.org/10.1016/j.petrol.2019.106666 [21] Wang, J. W., Du, J. X., Zhang, Y. C., et al., 2019. The Geological Conditions, Resource Potential and Exploration Direction in Nanpu Sag of Jidong Depression, Bohai Bay Basin. Marine Origin Petroleum Geology, 24(3): 21-28(in Chinese with English abstract). [22] Wang, X. G., 2013. Application of Self-Adaptive BP Neural Network to the Prediction of Shear Wave Velocity. Lithologic Reservoirs, 25(5): 86-88(in Chinese with English abstract). [23] Wang, Z., Zhou, X., 1982. Volcanic Petrology. Science Press, Beijing, 47-100(in Chinese). [24] Wood, A. B., Lindsay, R. B., 1956. A Textbook of Sound. Physics Today, 9(11): 37. https://doi.org/10.1063/1.3059819 [25] Wu, H. B., Wang, G. C., Hou, Y. P., et al., 2022. Seismic Identification of the Jurassic Volcano-Sedimentary series in the Hailar Basin and Lithofacies Palaeogeography Reconstruction. Earth Science, 47(8): 3056-3072(in Chinese with English abstract). [26] Xing, G. F., Li, J. Q., Duan, Z., et al., 2021. Mesozoic-Cenozoic Volcanic Cycle and Volcanic Reservoirs in East China. Journal of Earth Science, 32(4): 742-765. https://doi.org/10.1007/s12583-021-1476-1 [27] Xu, M. M., Yin, X. Y., Zong, Z. Y., et al., 2020. Rock-Physics Model of Volcanic Rocks, an Example from Junggar Basin of China. Journal of Petroleum Science and Engineering, 195: 107003. https://doi.org/10.1016/j.petrol.2020.107003 [28] Xu, S. Y., White, R. E., 1995. A New Velocity Model for Clay-Sand Mixtures 1. Geophysical Prospecting, 43(1): 91-118. https://doi.org/10.1111/j.1365-2478.1995.tb00126.x [29] Xu, S. Y., White, R. E., 1996. A Physical Model for Shear-Wave Velocity Prediction1. Geophysical Prospecting, 44(4): 687-717. https://doi.org/10.1111/j.1365-2478.1996.tb00170.x [30] Yan, J., Li, X. Y., Liu, E. R., 2002. Effects of Pore Aspect Ratios on Velocity Prediction from Well-Log Data. Geophysical Prospecting, 50(3): 289-300. [31] Yuan, C., Li, J. Y., Chen, X. H., et al., 2016. Quantitative Uncertainty Evaluation of Seismic Facies Classification: a Case Study from Northeast China. Geophysics, 81(3): B87-B99. https://doi.org/10.1190/geo2015-0228.1 [32] Zhang, B., Liu, C., Guo, Z. Q., et al., 2018. Probabilistic Reservoir Parameters Inversion for Anisotropic Shale Using a Statistical Rock Physics Model. Chinese Journal of Geophysics, 61(6): 2601-2617(in Chinese with English abstract). [33] Zhang, C., Ma, C. Q., Liao, Q., et al., 2009. Geochemistry of Late Mesozoic-Cenozoic Volcanic Rocks in the Huanghua Depression, Bohai Bay: Petrogenesis and Implications for Tectonic Transition. Acta Petrologica Sinica, 25(5): 1159-1177(in Chinese with English abstract). [34] Zhang, L. H., Pan, B. Z., Shan, G. Y., et al., 2020. Volcanic Alteration of Nanpu-5 Structure in Bohai Bay Basin: Effects on the Physical Property of Reservoir and Lower Limits of Physical Property. Geology and Resources, 29(4): 351-35629(4): 6(in Chinese with English abstract). [35] Zhang, L. H., 2009. Study on methods of evaluating igneous Reservoir of Logging. Jilin University(Dissertation). Jilin University, Changchun (in Chinese with English abstract). [36] Zhang, Y., Zhong, H. R., Wu, Z. Y., et al., 2020. Improvement of Petrophysical Workflow for Shear Wave Velocity Prediction Based on Machine Learning Methods for Complex Carbonate Reservoirs. Journal of Petroleum Science and Engineering, 192: 107234. https://doi.org/10.1016/j.petrol.2020.107234 [37] Zhao, H. B., Cheng, D. A., Li, L. L., 2009. Rock Physics Analysis of Deep Volcanic Rocks in Daqing Oilfield Beijing 2009 International Geophysical Conference and Exposition, Beijing, China, 24-27 April 2009. Beijing. [38] Zhao, P. F., Liu, P., Ming, J., et al., 2021. Distribution Characteristics of Volcanic Rock in CFD Oilfield and Its Controlling Effect on Reservoir. Earth Science, 46(7): 2466-2480(in Chinese with English abstract). [39] Zhu, X, J., 2011. Research on Logging Identification and Evaluation of Deep Volcanic Reservoirs in Nanpu 5th Structure(Dissertation). China University of Petroleum, Qingdao(in Chinese with Englishabstract). [40] Zimmermann, G., Burkhard, H., Melchert, M., 1992. Estimation of Porosity in Crystalline Rock by a Multivariate Statistical Approach. Scientific Drilling, 3(1-3): 27-35. [41] 白俊雨, 宋志翔, 苏凌, 等, 2012. 基于Xu-White模型横波速度预测的误差分析. 地球物理学报, 55(2): 589-595. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201202020.htm [42] 刘财, 乔汉青, 郭智奇, 等, 2017. 基于粒子群算法的页岩孔隙结构反演及横波速度预测. 地球物理学进展, 32(2): 689-695. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ201702032.htm [43] 乔汉青, 2017. 基于改进粒子群算法的横波速度预测方法研究(硕士学位论文). 长春: 吉林大学. [44] 王德滋, 周新民, 1982. 火山岩岩石学. 北京: 北京科学出版社, 47-100. [45] 王建伟, 杜景霞, 张永超, 等, 2019. 南堡凹陷石油地质条件、资源潜力及勘探方向. 海相油气地质, 24(3): 21-28. https://www.cnki.com.cn/Article/CJFDTOTAL-HXYQ201903003.htm [46] 王晓光, 2013. 自适应BP神经网络在横波速度预测中的应用. 岩性油气藏, 25(5): 86-88. https://www.cnki.com.cn/Article/CJFDTOTAL-YANX201305020.htm [47] 吴海波, 王国臣, 侯艳平, 等, 2022. 海拉尔盆地侏罗系火山-沉积岩地震识别和岩相古地理重建. 地球科学, 47(8): 3056-3072. doi: 10.3799/dqkx.2021.236 [48] 张冰, 刘财, 郭智奇, 等, 2018. 基于统计岩石物理模型的各向异性页岩储层参数反演. 地球物理学报, 61(6): 2601-2617. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201806036.htm [49] 张超, 马昌前, 廖群安, 等, 2009. 渤海湾黄骅盆地晚中生代-新生代火山岩地球化学: 岩石成因及构造体制转换. 岩石学报, 25(5): 1159-1177. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXB200905010.htm [50] 张丽华, 2009. 火成岩储层测井评价方法研究(博士学位论文). 长春: 吉林大学. [51] 张丽华, 潘保芝, 单刚义, 等, 2020. 渤海湾盆地南堡5号构造火山岩蚀变对储层物性影响及物性下限. 地质与资源, 29(4): 351-35629(4): 6. https://www.cnki.com.cn/Article/CJFDTOTAL-GJSD202004007.htm [52] 赵鹏飞, 刘朋, 明君, 田晓平, 刘文超, 吕世聪, 2021. CFD油田火山岩展布特征及其对油藏的控制作用. 地球科学, 46(7): 2466-2480. doi: 10.3799/dqkx.2020.238 [53] 朱学娟, 2011. 南堡5号深层火山岩油气藏测井识别与评价技术研究(硕士学位论文). 青岛: 中国石油大学(华东). -
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