Abstract

Most U.S. shale plays are spatially extensive with regions of different thermal maturity and varying production prospects. With increasing understanding of the heterogeneity, microstructure, and anisotropy of shales, efforts are now directed to identifying the sweet spots and optimum completion zones in any shale play. Rock typing is a step in this direction. We have developed an integrated workflow for rock typing using laboratory-petrophysical measurements on core samples and well logs. A total of seven wells with core data were considered for rock typing in the Woodford Shale. The integrated workflow has been applied in the Woodford Shale in a series of steps. In the first step, unsupervised clustering algorithms such as K-means and self-organizing maps were used to define the rock types. Rock type 1 is generally characterized by high porosity and total organic carbon (TOC). Rock type 2 had intermediate values of porosity and TOC and thus, moderate source potential and storage. Rock type 3 had the highest carbonate content, poor storage, and source rock potential. In the next step, a classification algorithm, support vector machines (SVM), was used to extend the rock types from the cores to the logs. A logging suite with gamma ray, resistivity, neutron porosity, and density logs was used for extending the rock types. These logs were used because they are commonly available and adequate to differentiate different rock types. The rock types were populated in the uncored sections of the seven cored wells and additionally in 12 wells (taken from Drilling Info) using a trained SVM model. Additional wells were taken to have sufficient data for production correlation. In the final step, a rock-type ratio (RTR) was defined based on the fraction of rock type 1 over the gross thickness. RTR was found to positively correlate with normalized oil equivalent production.

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