Unconventional SIG: Injectivity and Storage Potential Assessment of the Arbuckle Group for - Jun 2nd

Complete Title:  Injectivity and storage potential assessment of the Arbuckle Group for CO2 sequestration using supervised machine learning: Wellington Field, Kansas    Sponsored by TGS

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Meeting presentation will be from 12:00 noon to 1:00 pm

Speaker: Abidin Berk Caf, School of Geosciences, University of Oklahoma 

Co-Authors:  David Lubo-Robles, Matthew J. Pranter, Heather Bedle, Kurt J. Marfurt, Zulfiquar A. Reza,
School of Geosciences, University of Oklahoma
Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma

In this study, we assess the spatial variability of injectivity and storage potential of the Ordovician Arbuckle Group at the Wellington Field, Kansas. We defined three petrophysics-based rock types (petrofacies) from core porosity and permeability measurements using the flow-zone indicator approach. The petrofacies correspond to argillaceous dolo-packstone, very fine-grained dolo-packstone with low porosity and permeability, and medium-grained dolo-packstone with high porosity and permeability.  Using the common reflection-point gathers, we performed pre-stack seismic inversion and calculated various amplitude-versus-offset (AVO) attribute volumes. We used these seismic AVO attribute volumes as input for estimating a supervised seismic-scale 3D petrofacies and petrofacies probability volumes using a random forest algorithm.  The resulting 3-D rock type volume reflects the heterogeneous nature of porosity, permeability, and fluid flow characteristics within the Arbuckle Group. Additionally, spatial variability of fluid flow behavior was imaged in more detail, when compared to the conventional exocellular modeling with limited well control. Methods presented through this study are useful to reduce the uncertainty in the areas where the well control is limited. Furthermore, the outputs of this study can be utilized as input in seismic-constrained geostatistical modeling and simulation studies for modeling the behavior of CO2 plumes in the subsurface.

Speaker Biography: Abidin Berk Caf, School of Geosciences, University of Oklahoma
Abidin Caf is currently a Ph.D. candidate in Geophysics at the University of Oklahoma. His research is mainly focused on quantitative seismic interpretation and machine-learning applications in reservoir characterization and modeling. He also has a few years of industry experience as a reservoir geophysicist and a seismic interpreter. He is currently the president of the SEG OU student chapter and a member of SEG, AAPG, and the Geophysical Society of Oklahoma City. He holds an MSc in Geophysics from The University of Oklahoma and Geophysical Engineering from Ankara University, Turkey. 

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6/2/2022 12:00 PM - 1:00 PM
Central Daylight Time

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