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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