In an unconventional reservoir, a thorough understanding of the spatial distribution of the physical properties of rocks, in terms of facies, porosity, and permeability, is essential for realistic dynamic reservoir simulation and history matching. This paper provides a practical solution for enhanced reservoir performance analysis, combining the results of geological interpretation, 3D geostatistical electrofacies modeling, and flow simulation in an unconventional Eagle Ford shale play. The first step of the integrated approach is the application of hierarchical clustering methods to identify electrofacies groups using log curves. Next, electrofacies are converted into lithofacies through an analysis of core data. The 3D lithofacies and petrophysical distribution model is then created using stochastic geostatistical techniques. In the reservoir simulation step, the discretized facies model is constrained to assign geomechanical properties. Thus, a realistic fracture model is generated with a proper definition of fracture characteristics to control flow simulation and to enable better history matching. The solution presented in this paper provides an objective means of using the integrated approach in an accurate definition of fracture properties, in terms of length and orientation, for reservoir simulation and production forecasting in unconventional reservoirs.

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.