Seismic attributes, sedimentary petrography, and ultrasonic laboratory measurements in enhanced reservoir characterization: The thin viola carbonates, Kansas, U.S.A

Abstract

3D seismic data has revolutionized the oil and gas industry by providing fundamental access into the stratigraphy, geologic structures, and sedimentology of sedimentary basins worldwide. However, reliably identifying prospects, delineating hydrocarbon reservoirs, and understanding their properties rely on the quality and resolution of the seismic reflection data. Thin-layer seismic reflections interference in carbonate reservoirs characterization, which is known for rock heterogeneities and a significant share of hydrocarbon reserves, introduces additional uncertainty in relating seismic attributes variation to petrophysical properties and pore-fluid composition. The increase or decrease of seismic attributes, caused by thin-layer tuning “geometrical effect” interference between adjacent reflectors of geological layers, adversely impacts the utility of seismic attributes in characterizing rock properties for reservoirs. Thus, distinguishing seismic attribute anomalies diagnostic of petrophysical properties from geometric effects poses significant challenges of uncertainty. In this study, we integrate two-methods tunning-corrected seismic amplitudes with a set of microphotographs and ultrasonic P-wave velocities to better link reservoir rock properties to seismic amplitude variation of the thin Viola carbonate reservoir of Morrison NE Filed, Kansas, U.S.A.; lower ultrasonic P-wave velocities (lower peak reflection amplitude) of the reservoir facies core-plugs were observed. This study resulted in a better understanding of the characteristic seismic amplitude anomalies associated with good-quality reservoir properties of the Viola carbonate formation. Moreover, this study presents an effective data-driven method to isolate and remove thickness (tuning) effects on seismic horizon attributes. The proposed “detuning” approach employs two distinct methods: statistical and deterministic, each offering unique insights into the tuning phenomenon.