Rock Typing in Eagle Ford, Barnett, and Woodford Formations
- Ishank Gupta (University of Oklahoma) | Chandra Rai (University of Oklahoma) | Carl H. Sondergeld (University of Oklahoma) | Deepak Devegowda (University of Oklahoma)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2018
- Document Type
- Journal Paper
- 654 - 670
- 2018.Society of Petroleum Engineers
- K-Means, Rock Typing, Self Organizing Maps, Integrated Workflow, Data Mining
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- 682 since 2007
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Shales are the most commonly found sedimentary rocks on Earth. Most US shale plays are massive, with different maturity regions and varying prospects. There has been a paradigm shift in the understanding of shale anisotropy and microstructures in the last decade, with a high focus on the identification of sweet spots and optimal reservoir-quality zones. Rock typing is one of the sought-after techniques to achieve this objective, and it has become an integrated part of the unconventional-characterization work flow.
In this work, rock typing was performed with an integrated work flow using laboratory petrophysical measurements. The rock types were derived with machine-learning clustering algorithms—namely, K-means and self-organizing maps (SOM). The integrated work flow was applied in three different shale plays: 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, whereas Rock Type 3 had the lowest porosity and TOC.
Next, core-derived rock types had to be scaled up to logs. Support vector machines (SVM), a classification algorithm, was used for scaling up. It was trained with a data set consisting of depths at which both core and log data were available. Different logs such as gamma ray, resistivity, neutron, and density were used for scaling up. Finally, a rock-type ratio (RTR) was defined from rock-type logs based on fraction of Rock Type 1 over gross thickness. The ratio thus developed was found to have a strong correlation with normalized oil-equivalent production rate.
In total, 22 wells with core data were considered for rock typing in the three shale plays. The rock types were scaled up to 95 wells at a cumulative over a 20,000-ft depth interval. The work flow shown in this paper can easily be extended to other data sets in other plays. The manual approach, on the other hand, can be prohibitively time-consuming.
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