Petrophysical Study of Shale Properties in Alaska North Slope Minh Tran Tapan Mukerji Energy Resources Engineering Department Stanford University, CA, USA
Region of Interest 1.5 miles 20 miles
Stratigraphic Column
Motivation Characterize elastic properties of different North Alaskan shale lithofacies. Validate existing rock physics models in the literature by comparing to other shale plays in the United States. Integrate geochemical analysis into quantitative seismic interpretation. Quantify seismic signature to evaluate source rock potential in the region of interest.
Quantitative Seismic Interpretation Workflow Post-processing Seismic Data (partial or full pre-stack) Well Log, Core, Thin Section Geochemical Analysis Rock physics model Inverted Seismic Data of Elastic Properties (AI, EI, AVO cube) Training Data of Each Lithofacies (Vp, Vs, Density) Source Rock Properties (TOC, HI, Ro) Calibration of multi-scale data Facies-dependent Relationship between Elastic and Geochemical Properties Statistical Classification Evaluation of source rock potential in terms of Geochemical and Petrophysical Properties Acoustic Impedance: AI=Vp*Density Elastic Impedance: EI=Function (Vp, Vs, Density, Angle)
Scope of Research Build a reliable training data set of elastic properties (Vp, Vs, density) of different shale units in Alaska North Slope (Hue/HRZ, Pebble, Kingak, Shublik) based on log analysis. Measure shale elastic properties (Vp, Vs) of core plugs in laboratory to calibrate logging measurements and characterize shale anisotropy in terms of wave propagation. Establish relationship between elastic properties and source rock credentials (Total Organic Content, Hydrogen Index, thermal maturity).
Available Data Log suite of Alcor and Merak: Gamma Ray tool. Density tools, Acoustic tools. Cored interval: Alcor 1: 117 feet (Hue, Shublik) Merak 1: 398 feet (Hue, HRZ, Kingak, Shublik) Core plug measurement:
Petrophysical Diagnostics by Well Log Analysis Intervals of interest: 8500-11000 feet MD in each well. Purpose: to delineate different lithofacies in terms of petrophysical and elastic properties. Methodology: crossplot of different properties to separate clusters of data of each lithofacies.
Merak Petrophysical Diagnostics HUE/HRZ HUE HRZ PEBBLE KINGAK SHUBLIK
Lithofacies Definition SHUBLIK SHUBLIK & HUE HUE HRZ pebble KINGAK SHUBLIK KINGAK & pebble SHUBLIK
Lithofacies Definition SHUBLIK HUE KINGAK & pebble HRZ HUE HRZ pebble KINGAK SHUBLIK
Lithofacies Definition HUE HRZ pebble KINGAK SHUBLIK SHUBLIK HUE KINGAK & pebble
Lithofacies Definition HUE HRZ pebble KINGAK SHUBLIK SHUBLIK HUE KINGAK, HRZ & pebble
Shale Anisotropy Property depends on direction. Seismic wave sees shale anisotropy: Direction of propagation (P and S-wave) Direction of polarization (within S-wave: SH and SV). Defined at the scale of the measurement (core plugs or log). Affects seismic signature (amplitude) of multi-azimuth seismic data. Make use of Amplitude-versus-offset seismic data. Shear wave polarization (Hyperphysics.edu) Direction of propagation
MERAK HUE Shear Splitting in Sonic Logs
Core Selection At least one plug per lithofacies. Avoid undesirable lithology (calcite bands, pyrite inclusion ). Avoid visible fracture. 3 different directions at each selected depth (horizontal, vertical and 45º to the bedding plane) if possible Similar texture and depth.
Elastic Properties vs Geochemical Properties SHUBLIK KINGAK HUE HRZ Prasad et al. 2009
Correlation with other Geochemical Properties
Correlation with Geochemical Properties SHUBLIK KINGAK & HRZ (Alfred and Vernik 2012)
Summary Different shale lithofacies in North Alaska System can be qualitatively delineated in terms of elastic and petrophysical properties. Alaskan North Slope shale anisotropy is apparent in sonic log but needs to be verified by core measurements. Crossplots between elastic properties and TOC or HI show good separation of different lithofacies. Existing petrophysical model for shale can be applied if properly calibrated.
Future Work Perform velocity measurement (bench-top and varying confining pressure) on Stanford core set. Update geochemical data with GeoMark data. Calibrate the log-derived training data set from core measurements. Expand training dataset by using existing rock physics model for shale (fluid substitution). Build quantitative rock physics model for Alaska North Slope shale.
Basin 22 and Petroleum System Modeling Industrial Affiliates Program
Questions?
References Passey, Q.R., Creaney, S., Kulla, J.B., Moretti, F.J. & Stroud, J.D., 1990, A practical model for organic richness from porosity and resisitivity logs: American Association of Petroleum Geologists Bulletin 74(12), 1777-1794. Alfred, D. & Vernik, L, 2012, A new petrophysical model for organic shales: Society of Petrophysicists and Well Log Analysis. Prasad, M., Kenechukwu, C., McEvoy, E. & Batzle M.L., 2009, Maturity and impedance analysis of organic-rich shales: SPE 123531.