XRF data originally collected from 13 wells (then increased to 26 wells), and sampled from drill-cuttings at 5m (16ft), were analyzed and compared with raw log data that was re-sampled to the same 5m intervals. Correlations between a number of mineralogical indicators and the raw logs revealed spatial trends in the data, both from a vertical stratigraphic profile as well as geographically. This was used to generate a stochastic, 3D geo-model tying in more than 140 wells to populate the model with key parameters. Normalized elemental anomalies were correlated with log values in order to establish facies criteria based on both lithological provenance and petrophysical cut-offs, thereby highlighting what are believed to be more frac-able shale. The resulting summary statistics and probability maps of interest, capture more accurately geo-spatial distribution of key mineralogical indicators, and thus help to better quantify estimates of potential hydrocarbon volumes and targeted locations early in the exploration phase.
An Innovative Workflow to Refine Exploration Phase Assessment of Unconventional Prospects; Using XRF Analysis Data and 3-D Geo-Modeling Techniques
Marechal, Francois C., Welsh, Jennifer , Porter, Scott , Ghanbari, Sasan , and Raj M. Damodaran. "An Innovative Workflow to Refine Exploration Phase Assessment of Unconventional Prospects; Using XRF Analysis Data and 3-D Geo-Modeling Techniques." Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Denver, Colorado, USA, August 2014. doi: https://doi.org/10.15530/URTEC-2014-1922152
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