Thin layer effects on horizon attributes: analysis beyond amplitude tuning, Viola carbonate reservoir, Morrison NE field, Kansas - Jallow, Muhammed (Kansas State University, 2024)
Three-dimensional seismic reflection data has revolutionized the oil and gas industry by enabling vital insights into stratigraphy, geologic structures, and reservoir properties in sedimentary basins worldwide. However, accurately identifying prospects, delineating hydrocarbon reservoirs, and understanding their properties rely on the quality and resolution of seismic data. 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 reservoir characterization. Thus, distinguishing seismic attribute anomalies diagnostic of petrophysical properties from geometric effects poses significant challenges and uncertainties. Traditionally, seismic data tuning correction has focused primarily on amplitude attributes, neglecting the impact on other crucial attributes, such as frequency, which reservoir geometry can also affect. This narrow scope limits the effectiveness of conventional methods in providing a holistic understanding of subsurface properties. This study presents an effective data-driven method to isolate and remove thickness (tuning) effects on seismic horizon attributes. The proposed approach employs three distinct methods: statistical, deterministic wedge model, and machine learning, each offering unique insights into the tuning phenomenon. CO₂ sequestration/enhanced oil recovery injection can be associated with reservoir seismic time-thickness changes that affects the seismic response due to the mixing with and displacement of hydrocarbon oil, changes in subsurface reservoir pressure, and fluid composition distribution. The response of thin layer effects on seismic attributes offers enormous potential in monitoring CO₂ sequestration and enhanced oil recovery programs. The analysis of detuned seismic attributes of the Viola reservoir in Morrison NE field, reveals promising areas for future well placement. Areas characterized by structural highs, substantial thicknesses, and persistently low amplitudes after detuning hold significant potential for hydrocarbon production.
Keywords: Seismic attributes; Tuning; Seismic resolution; Machine learning; Viola carbonate reservoir; Thin layer