Shales are the most commonly found sedimentary rocks on earth. Most US shale plays are massive with different maturity regions and varying prospects. There is a paradigm shift in the understanding of shale anisotropy and micro-structure in the last decade. The focus has now shifted on identifying the sweet spots and optimum zones for completion. Rock Typing is one of the sought-after techniques to achieve this objective and it has become an integrated part of the unconventional characterization workflow.
In this work, rock typing was done using an integrated workflow utilizing laboratory petrophysical measurements. The rock types were derived using machine learning clustering algorithms namely K-means and Self Organizing Maps (SOM). The integrated workflow was applied in three different shale plays namely Eagle Ford, Barnett, and Woodford.
Three different rock types were identified. In general, Rock Type 1 had the highest porosity and Total Organic Carbon (TOC) indicative of highest storage and source rock potential, respectively. Rock Type 1 was also the key rock type controlling the production. Rock Type 2 had intermediate porosity and TOC while Rock Type 3 had the lowest porosity and TOC.
Next, core derived rock types had to be upscaled to logs. Support Vector Machines (SVM), a classification algorithm was used for upscaling. It was trained using a dataset consisting of depths at which both core and log data were available. Different logs like gamma ray, resistivity, neutron and density were used for upscaling. Finally, a Rock Type Ratio (RTR) was defined from rock type logs based on fraction of Rock Type 1 over gross thickness. The ratio so developed was found to have a strong correlation with normalized oil equivalent production rate.
A total of 22 wells with core data were considered for rock typing in the three shale plays. The rock types were upscaled to 95 wells at a cumulative over 20,000 ft. depth interval. The workflow shown in this paper can easily be extended to other datasets in other plays. The manual approach on the other hand can be prohibitively time-consuming.
