Rock Typing in Wolfcamp Formation
- Ishank Gupta (The University of Oklahoma) | Chandra Rai (The University of Oklahoma) | Carl Sondergeld (The University of Oklahoma) | Deepak Devegowda (The University of Oklahoma)
- Document ID
- Society of Petrophysicists and Well-Log Analysts
- SPWLA 58th Annual Logging Symposium, 17-21 June, Oklahoma City, Oklahoma, USA
- Publication Date
- Document Type
- Conference Paper
- 2017. copyright held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors
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High productivity wells are obtained by drilling in sweet spots and having optimum fracture treatments. Reservoir characterization is key to identify both the sweet spots and optimum completion zones. Rock typing is an integral part of the reservoir characterization workflow which identifies different flow units. In this work, we present an integrated workflow for rock typing using lab petrophysical measurements and well logs acquired from six wells in the Wolfcamp formation in the Permian basin. Only three wells had cores. The petrophysical measurements were sampled every 3 feet interval from 900 feet of continuous core recovered from these three wells. Unsupervised clustering algorithms like K-means and Self Organizing Maps (SOM) were used to define rock types.
Rock Type 1 is characterized by the highest porosity (7-10%) and TOC (4-6%). Not surprisingly, Rock Type 1 had the highest positive impact on well productivity. Rock Type 2 had intermediate values of porosity (6-8%) and TOC (3-4.5%) and moderate source potential and storage. Rock Type 3 had the highest carbonate (60-80%) content and poor storage and source rock potential (2-4% TOC, 4-7% porosity). Rock Type 3 was the worst rock type.
A classifier from well log data that is conditioned to core data was created. The classifier was applied at depth locations where core data were not available. The classifier included GR and neutron porosity logs. These logs were used as these were the commonly available logs in all the wells used in the study. We define a rock type ratio (RTR) based on the fraction of Rock Type 1 over total perforation. RTR was found to strongly correlate with oil production rate. The advantage of the workflow developed here is that it can easily be generalized to diverse data types and other plays in different geologic settings.
The improvement in the rate of success of wildcat drilling from 75% in 1974 to 95% in 2010 has been widely attributed to improvements in reservoir characterization techniques (Williams 2008). One of these techniques, rock typing, is central to meaningful interpretation of the diverse data types acquired over diverse length scales and with varying resolutions. This is true for both conventional and unconventional reservoirs.
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