Seismic Azimuthal Anisotropy and Fracture Analysis from PP Reflection Data
Many of the reservoirs, such as carbonates, tight clastics and basement reservoirs, are often fractured. In oil and gas exploration and development, one may require the delineation of the distribution and orientation of fractures. Fractures can not only provide pore space to hold oil and gas in place, but can also increase permeability to provide a pathway for fluid flowing from reservoir to well locations. There are three existing methods for extracting fracture information from PP seismic data. They are: (1) NMO velocity method, (2) residual moveout method, and (3) amplitude method. Each of them has advantages and disadvantages.
All three existing methods have some limitations, as some factors influence the precision and accuracy of the results of fracture analysis. A dipping reflector may induce "false" azimuthal anisotropy of the seismic amplitudes. Furthermore, in structural areas, detecting fractures from unmigrated CMP gathers will misposition fracture information. Therefore, migration must be incorporated into fracture analysis. Because the widely used common-offset migration will smear the incident angles, prestack common-angle time migration was developed in this dissertation and tested on synthetic and field data. The prestack common-angle migration solves smearing of incident angle, mispositioning and anisotropy induced by dipping reflectors simultaneously.
A new method, inversion, was developed. It is an integration of the NMO velocity method and the moveout method for extracting Thomsen's parameter, (v), from the residual moveout on the bottom of the fractured layer.
A practical workflow for fracture analysis using PP reflection data is presented in this dissertation. Both the amplitude method and the inversion are employed in the workflow. The amplitude method gives detailed information on every time sample. In contrast, the inversion gives the information for the entire fractured layer. This workflow was successfully applied to both physical modeling data and field data. The results match the original model and the well production rates.