三维地震与地面微地震联合校正方法

由于地面微地震监测台站布设在地表,会受到地表起伏、低降速带厚度和速度变化的影响,降低了微地震事件的识别准确度和定位精度,限制了地面微地震监测技术在复杂地表地区的应用.因此,将三维地震勘探技术的思路引入到地面微地震监测中,提出了三维地震与地面微地震联合校正方法,将油气勘探和开发技术更加紧密地结合在一起.根据三维地震数据和低降速带测量数据,通过约束层析反演方法建立精确的近地表速度模型,将地面微地震台站从起伏地表校正到高速层中的平滑基准面上,有效消除复杂近地表的影响.其次,根据射孔数据和声波测井速度信息,通过非线性反演方法建立最优速度模型,由于已经消除复杂近地表的影响,在进行速度模型优化时不需要考虑近地表的影响,因而建立的速度模型更加准确.最后,在精确速度模型的基础上,通过互相关方法求取剩余静校正量,进一步消除了复杂近地表和速度模型近似误差的影响.三维地震与地面微地震联合校正方法采用逐步校正的思路,能够有效消除复杂近地表的影响,提高微地震数据的品质和速度模型的精确度,保证了微地震事件的定位精度,具有良好的应用前景.

Joint correction method based on 3D seismic and surface microseismic data

Microseismic monitoring has been proven to be a key technology to optimize hydraulic fracture stimulation of unconventional reservoirs. The use of surface station arrays for microseismic monitoring is a relatively new development with applications in some fields including hydraulic fracture delineation, reservoir stress mapping and seismic hazard analysis. Seismic waves are usually influenced by the complex near surface when microseismic signal propagates from the source to array stations on the surface. Microseismic data quality can decrease with static correction problems of complex near surface, which further lowers recognition and location accuracy of microseismic events. Thus the scope of surface microseismic is limited by complex near surface. For 3D seismic data acquired previously in monitoring areas, tomographic inversion methods are introduced to tackle static correction problems of complex near surface. A joint correction method is proposed in surface microseismic monitoring, which is based on 3D seismic and microseismic perforation data. This method includes three steps such as tomographic inversion static correction, velocity model correction and residual static correction. The first step is tomographic inversion static correction by 3D seismic data in the monitoring area. The velocity model of complex near surface can be obtained by the constrained tomographic inversion method with 3D seismic data. The static correction values of microseismic stations can be calculated by the near surface velocity model, and can be used to correct stations to a datum level in the high-velocity layer. The second step is velocity model correction by microseismic perforation data and logging data. Through the nonlinear inversion method to establish the optimal velocity model, the velocity model doesn't need to consider the effects of near surface, which is the most accurate velocity model. The third step is residual static correction by microseismic perforation data. The residual static correction values of stations can be obtained by the cross-correlation method. By research and practice, the joint correction method is proposed for enhancing surface microseismic monitoring, which is based on 3D seismic data and microseismic perforation data. The joint correction method is used in microseismic data processing, which is an effective way for static correction and velocity model correction problems. Static correction and velocity correction problems have been resolved by the joint correction method effectively. The joint correction method can greatly improve the quality of microseismic data, and effectively resolve static correction and velocity correction problems, which are useful for recognition and enhancing location accuracy of microseismic events.