Like many unconventional plays, the Eagle Ford, once one of the most active shale plays in the world with over 250 rigs running, saw a vast amount of data collected during the boom over a very short time. As with most unconventional resources, a lack of validation of reservoir parameters prevailed in the early history of these plays (emerging plays) and thus, hypothesis drove drilling and completion optimization programs. The 2015 drop in commodity prices accelerated the need to optimize well designs and spacing and stacking patterns in a less capital-intensive manner. A sector model was built that enabled discrete modeling of the 4 development wells in place and significant remaining undeveloped potential to be completed both within and near the sector model area. From this model, substantial understanding around the key parameters driving subsurface performance both from the rock and wellbore design perspectives was gained. As in-fill drilling has occurred in other areas of the play, a learning curve developed around the understanding of vertical connectivity, fracture geometry, well interference and the impact of clusters and job size on fracture contact with the reservoir. This learning curve has been applied to the integrated model to understand what an optimized infill drilling program for the area would look like at various hydrocarbon pricing scenarios.
This paper utilizes an integrated model approach to understand reservoir performance on a pad with four wells completed across multiple horizons in the Eagle Ford. Wireline quad combo compressional and shear log suites (including azimuthal anisotropy and VTI sonic processing, resitivity/acoustic borehole imagers, and NMR), core (geomechanical, geochemical analysis, routine core analysis and specialized core analysis), completion data (fracture treatments with pre-and post-job shut-in pressures), production data (1200 days of production history with a bottom-hole pressure gauge and calculated bottom hole pressures from rod pumps) are used to build petrophysical models, geo-models, geomechanical models, fracture propagation models and reservoir models with the aim of understanding completion and production drivers. A workflow is presented that enables these models to improve our understanding of layering effects (vertical connectivity), fracture asymmetry (pressure sinks or sources), well interference (hydraulic vs. propped lengths) and the impact of clusters and job size on fracture contact with the reservoir.