复杂多矿物组分的页岩气储层横波时差预测方法

测井资料是获取页岩气储层物性参数、矿物组分、总有机碳含量、含气量等关键评价参数的基础资料,在测井资料的工程应用方面,一般通过实际测井的地层纵、横波时差来计算岩石的杨氏模量、泊松比、破碎压力等力学参数,部分页岩气新井和大量老井中无横波时差资料,致使无法计算页岩气地层的岩石力学和脆性指数。为此,把已有的针对简单矿物组分的砂泥岩地层横波时差预测方法拓展应用到复杂多矿物页岩气储层,在准确处理出页岩气储层复杂矿物组分的基础上,基于孔隙介质岩石物理模型和Biot—Gassmann方程,应用VRH模型求得地层的等效弹性模量,再以纵波时差作为约束参数来反演预测页岩气储层的横波时差。应用效果表明:该方法预测结果与实测结果对比误差较小。同时还探讨了区域纵横波时差回归统计法、简化的基于纵波时差来预测横波时差等多种方法预测结果的差异和影响因素,最终优选出的适合页岩气储层的横波预测方法,满足了生产现场解释评价的需要。

The S-wave time-difference prediction method for complicated mineral shale gas reservoirs

Logging data is the basic information in interpreting key evaluation parameters such as reservoir physical properties, mineral components, TOC, gas content, etc. In the aspect of logging data engineering application，P-wave and S-wave time-difference from logging are usually used to calculate rock mechanical parameters such as Young's modulus, Poisson ratio, broken pressure and so on, but the lack of S-wave time-difference data for many new and old shale gas wells makes it impossible to work out rock mechanical and brittleness parameters of shale gas reservoirs. Therefore, the existing S-wave time-difference prediction method for the simple mineral sand and shale reservoirs will be also applied to complicated mineral gas reservoirs. Based on the accurate interpretation of shale complicated minerals, porous rock physical model and Biot-Gassmann equation，the S-wave time-difference prediction method was used to calculate the equivalent elastic module of shale layer with VRH model, and the S-wave time-difference was inversely predicted by using the P-wave time-difference as a constraint parameter, so the errors between the results predicted by this method and measured results are small. In addition, the differences of several methods such as regression statistics of regional S-wave time-difference and P-wave time-difference, simplified S-wave time-difference prediction based on P-wave time-difference were compared and factors affecting them were analyzed, and finally the method suitable for S-wave time-difference prediction of shale gas reservoirs was selected, which meets the demand of shale gas reservoir interpretation on site.