Rock Classification in the Haynesville Shale-Gas Formation Based on Petrophysical and Elastic Rock Properties Estimated From Well Logs

Abstract

Rock classification can enhance fracture treatment design for successful field development in organic-shale reservoirs. Rock brittleness and elastic properties are important parameters to be considered in selection of the best candidate zones for fracture treatment. However, characterization of the rapid variation of petrophysical, compositional, and rock elastic properties in organic-shale reservoirs is challenging. Well logs can be applied for real-time assessment of these formation properties at relatively high resolution compared to core measurements. In this paper, we introduce a rock classification technique in shale-gas reservoirs that takes into account well-log-based estimates of rock elastic properties as well as mineralogy, kerogen porosity, and organic-richness.

We first conduct a joint interpretation of well logs and core measurements to estimate depth-by-depth petrophysical and compositional properties of the shale-gas reservoir. The self-consistent approximation model is applied to estimate rock elastic properties. This model enables the assessment of elastic properties based on the estimated mineralogy and the inclusion shapes. We then calculate rock brittleness index from the estimated elastic properties. Rock classes are determined using estimated rock brittleness, mineralogy, total organic content. Finally, kerogen porosity is incorporated in the rock classification to further improve the identified rock types.

We applied the introduced rock classification technique in the Haynesville shale-gas formation. The identified rock classes were cross-validated with the existing core images. Unlike core-based rock classification techniques in organic-shale reservoirs, the introduced method in this paper is mainly based on well logs and can provide reliable rock classes without requiring a large core database. An efficient well-log-based rock classification technique can potentially enhance fracture treatment and production from complex organic-shale reservoirs through (a) detecting the best candidate zones for fracture treatment and (b) optimizing the number of required fracture stages.