The Haynesville Shale remains a prolific gas resource amongst the Unconventional Plays in the US. The continued viability and the commercial success of the play are highly dependent on the optimization of field development plans through drilling, completions, and production improvements.
This paper presents an integrated solution that includes geologic, geophysical, and geomechanical properties. The workflow includes a Discrete Fracture Network (DFN), modeled hydraulic fractures, and well diagnostics data, to improve the understanding of the subsurface. The goal is to provide valuable input to optimize the development plan and completions strategy in the Haynesville Shale.
The development of the integration platform (3D geo-cellular model) involved detailed seismic interpretation based on a sequence stratigraphic framework, definition of stratigraphic-mechanical units, and incorporation of a robust petrophysical analysis set in a structurally controlled grid. The structural framework of the model was enhanced using over 100 carefully interpreted geo-steered horizontal wells to improve accuracy and grid calibration to the well paths. The natural fracture analysis included core description and fracture counts complemented by borehole image data, which coupled with a geomechanical stratigraphic characterization study, assisted in understanding the field wide fracture intensity distribution and orientation.
The hydraulic fracture conductivity and net pressure profiles, along hydraulic fracture planes, were developed using a planar geometry fracture simulator. The results served as input to the geomechanical model and as the basis for hydraulic fracture stage design setup in the dynamic model.
A 3D geomechanical model was constructed using the geologic model, based on the pore pressure and mechanical properties from calibrated 1D-geomechanical models. Computational geomechanical simulations allowed us to identify reactivated natural fractures, which produced synthetic-microseismic events, and the Critically Stressed Fracture Volumes (CSFV). These inputs were used in the subsequent identification of Stimulated Rock Volumes (SRV). Interpretations are supported by tracer data and other field observations that assisted in establishing inter-well connectivity.
The products from these processes will be incorporated into a reservoir simulation model. History matching of production data will be conducted for validation and refinement. History matched models will be used to identify and evaluate the impact of key drivers of optimization studies to various field development scenarios in order to enhance well completion and well spacing strategies in the development plan.