Improved Fracture Productivity Prediction Using Enhanced Seismic Attributes

Abstract

Production from unconventional reservoirs is influenced by well spacing and induced fracture placement as well as the interaction between hydraulic fractures and in-place natural fracture systems. The purpose of modeling of these complex systems is to evaluate how production can be maximized while maintaining operational efficiencies, which promote reduced well pad footprints and effective fracture stage placement. Comingled flow conduits in unconventional reservoirs exist as amalgamated fracture systems, and multidisciplinary characterization with analysis from geologists, geophysicists, and engineers is necessary to maintain a consistent subsurface representation. To extend model fidelity in the description of complex fracture systems, a workflow was developed to evaluate the spatial constraint of natural fractures based on use of a fault indicator in cases where correlation exists between faults and fractures as a result of exerted structural controls. Use of the fault likelihood attribute in the development of an unconventional reservoir confirms that some of the better producing wells have been completed near heavily faulted zones; however, such increased productivity can also be hindered when pressure communication is established between wells associated with the same fault block. An examination of an Eagle Ford formation was conducted, highlighting how a consistent subsurface description not only enabled increased efficiency in future wells and hydraulic fracture placement but also promoted reduced drilling and completion costs as well as increased field productivity. This was achieved by combining fault likelihood constrained natural fracture network (NFN) as well as dynamic simulation of the stimulated and external reservoir volume, incorporating a petro-elastic model (PEM) to preserve geologic continuity between seismic attributes and the simulation.

Visualization of drained rock volume (DRV) in hydraulically fractured reservoirs with and without natural fractures using complex analysis methods (CAMs)

“…Several authors have previously used techniques such as fault likelihood attribute (Hale 2013;Lomask et al 2017;Wu and Hale 2015;Ramsay et al 2017) to interpret 3D seismic images and generate a spatial constraint of the natural fractures. Others have recommended a stochastic approach based on geostatistical analysis of the 3D seismic images (Admasu et al 2006).…”