Fast determination and characterization of formation resistivity anisotropy, dip, and fracture using multicomponent induction data
Fast determination and characterization of formation resistivity anisotropy, dip, and fracture using multicomponent induction data (in Fractures, Mehdi E. Far (prefacer), Enru Liu (prefacer) and Jon Downton (prefacer))
Interpretation (Tulsa) (August 2015) 3 (3): ST55-ST71
Multicomponent induction (MCI) logging measurements have been widely used in the past decade for determining formation resistivity anisotropy (horizontal and vertical resistivities: R (sub h) and R (sub v) ), dip, and azimuth. Currently, almost all MCI processing and interpretation algorithms of determining R (sub h) , R (sub v) , dip, and azimuth are based on simplified transversely isotropic (TI) formation models. In most geologic environments, formations are layered or laminated, making the TI model a reasonable assumption. Subsurface formations usually contain different types of fractures (natural or drilling-induced), and exhibit azimuthal resistivity anisotropy in the bedding plane, which leads to formation biaxial anisotropy (BA) in the same bedding plane. (This type of media is usually called orthorhombic or orthotropic in mechanical engineering and geomechanics.) Therefore, MCI data processing based on TI models may not be valid in complex BA formations caused by fractures. MCI processing and interpretation methods based on BA formation models are needed for more accurate descriptions of complex anisotropic formations. Fractures significantly affect fluid flow in formations, and therefore the fracture characterization with MCI logging can provide some useful information for oil/gas development and production, especially in unconventional reservoirs. We have developed a fast and practical integrated method of borehole multiarray MCI data processing for effective determination of formation BA anisotropy (or triaxial resistivities: R (sub x) , R (sub y) , and R (sub z) , dip, and azimuth. The multiple MCI data sets were further applied to fracture evaluation, and they were tested with synthetic and field log data sets. The method has the following components: the inversion algorithm based on the multiple BA models, a fracture identification function for detection of the fracture, and the corresponding approach for estimation of the fracture relative azimuth and dip angle. The application results demonstrated that accurate triaxial formation anisotropy and dip can be obtained based on the BA models compared with the TI processed logs. Furthermore, fractures can be characterized by integrating measurements and processed log data, such as the recovered horizontal resistivities R (sub x) and R (sub y) , vertical resistivity R (sub z) , and formation dips based on the BA and TI models.