考虑开发动态的定性经验出砂动态预测　　

Research on qualitative experimental dynamic sanding prediction considering the development performance

赵益忠 孙德旭 梁伟 王勇 陈雪

ZHAO Yizhong SUN Dexu LIANG Wei WANG Yong CHEN Xue

中石化胜利油田股份公司采油工艺研究院，山东东营　257000

Oil Production Technology Research Institute, Shengli Oilfield, Sinopec, Dongying 257000, China

储层出砂是一个动态发展的过程，仅凭开发初期的测井资料开展定性经验出砂静态预测难以满足开发需要，应综合考虑开发动态资料来开展出砂动态预测。首先，在模拟三轴应力环境下进行了不同含水饱和度及孔隙压力变化影响下的疏松砂岩纵波波速测试，研究表明随含水饱和度增加，纵波波速逐渐增加，且胶结越弱，含水饱和度对纵波波速影响越显著；随储层孔隙压力降低，纵波波速逐渐增加，且胶结越强，孔隙压力变化对纵波波速影响相对越小；其次，采用二元二次函数拟合及最优化原理建立了含水饱和度及无因次孔隙压力变化综合影响的纵波波速模型，在此基础上引入储层压力亏空、含水率及岩性影响因子，发展了经典的组合模量模型，建立了定性经验出砂动态预测模型。加蓬Obangue油田现场应用分析表明，该模型具有较好的可靠性和实用性。

Sanding from the payzone is a dynamic developing process. It’s difficult to meet the needs of development for making a reliable qualitative experimental sanding prediction just by static logging data in the early development periods. Dynamic development data should be comprehensively considered for directing dynamic sanding prediction. First, the longitudinal wave velocities of unconsolidated sandstones which have different water saturations and pore pressures have been tested in simulated triaxial stress environment. The research shows that the longitudinal wave velocities speed up with the increasing of the water saturations; moreover the water saturation has much more significant effect on wave velocity when the sand consolidation is weaker. On the other hand, the longitudinal wave velocities increase with the decreasing of the formation pore pressure; and the stronger the consolidation, the weaker effect the pore pressure changes influence on the longitudinal wave velocities. Second, longitudinal wave velocity model which comprehensively influenced by water saturations and dimensionless pore pressure changes has been established by binary quadratic function fitting and principle of optimality. Depletion of reservoir pressure, water cut and lithology impact factor was introduced on the previous basis to develop the traditional combined modulus model and to establish the dynamic sanding prediction model. Last, the application in Obangue oil field shows that the dynamic sanding prediction model has well reliability and practicability.