Thin, highly interbedded sand-shale tight gas plays are generally characterized by a low resistivity response and considerable variation in pore-size distribution, and require reservoir stimulation to make them economically viable. The Upper Jurassic and Lower Cretaceous Lower Cotton Valley formation in north Louisiana/east Texas is one such play. This stacked clastic reservoir provides significant challenges determining the best zone to be targeted for horizontal well hydrocarbon exploitation. The presence of clay in the intergranular spaces and in the matrix of the rock affects the cementation exponent parameter that is used to estimate water saturation. The smaller and varying pore-size distribution in these rocks affects reservoir deliverability and variation in bound vs. free fluids.

This paper showcases the development and application of an evaluation workflow that addresses the reservoir characterization challenges. Local experience has demonstrated that a standard triple combo log is generally inadequate to address these problems. An advanced logging suite was deployed that consisted of a triple combo tool, a neutron-capture mineralogy tool, a high-frequency dielectric wireline tool, a magnetic resonance imaging tool, a dipole sonic tool, and an advanced wireline resistivity tool that can resolve horizontal and vertical resistivity simultaneously.

The developed workflow was applied to rank the horizontal well producibility potential of the stacked sands. Routine and special core analysis was used to calibrate the log-based interpretation models. The interpreted mineralogy was used to enter the matrix dielectric constant to the dielectric interpretation, which was used to provide an estimate of a variable cementation exponent confirmed by core data. Nuclear magnetic resonance (NMR) data was used to estimate clay-bound water, capillary-bound water, and pore-size variation based permeability. A novel feature of the horizontal, vertical resistivity interpretation was to use the clay (rather than shale) calibrated to core data to predict the sand resistivity. The workflow also included a production prediction for different induced hydraulic fracture half-lengths, which further assists in the target selection.

The workflow was applied to multiple wells from which different sands were selected as the best targets.

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