Rock Properties, Seismic Modeling, and 3c Seismic Analysis in the Bakken Shale, North Dakota
Paris Castellano, Andrea Gloreinaldy 1990-
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A solid understanding of the factors that affect the seismic velocity and the amplitude variation with offset (AVO) is imperative for a reliable interpretation of seismic data and related prospect de‐risking. To understand the relationship between rock properties and their elastic response (i.e. velocity and density), petrophysical properties, rock‐physics, seismic modeling, and fluid substitution are analyzed. Seismic inversions and statistical predictions of rock properties are integrated to delimit prospective intervals and areas with high total organic carbon (TOC) content within the Bakken Formation, North Dakota. The shale intervals can be recognized by cross‐plotting well logs velocities versus density. The hydrocarbon potential is observed on logs as low densities, high gamma‐ray response, low P and S‐wave velocities, and high neutron porosities. Organicrich intervals with TOC content higher than 10 wt. % deviate from the ones that have lower TOC in the density domain, and exhibit slightly lower velocities, lower densities (< 2.3 g/cc), and a generally higher shale content (> 40%). Within the study area, Well V‐1 shows the highest TOC content, especially at the Upper Bakken depths with approximately 50% of clay volume. TOC is considered to be the principal factor affecting changes in density and P and S‐wave velocities in the Bakken shales. Vp/Vs ranges between 1.65 and 1.75. Synthetic seismic data are generated using the anisotropic version of Zoeppritz equations including estimated Thomsen parameters. For the tops of Upper and Lower Bakken, the amplitude becomes less negative with offset showing a negative intercept and a positive gradient which correspond to an AVO Class IV. A comparison between PP and PP‐PS joint inversions shows that the P‐impedance error decreases by 14% when incorporating the converted‐wave information in the inversion process. A statistical approach using multi‐attribute analysis and neural networks allows to delimit the zones of interest in terms of P‐impedance, density, TOC content, and brittleness. The inverted and predicted results show fair correlations with the original well logs. The integration between well‐log analysis, rock‐physics, seismic modeling, constrained inversions and statistical predictions contribute in identifying the vertical distribution of good reservoir quality areas within the Bakken Formation.