Poro-Acoustic Impedance (PAI) as a new and robust seismic inversion attribute for porosity prediction and reservoir characterization

• Published in: Journal of applied geophysics Vol. 223; p. 105351
• Main Authors: Leisi, Ahsan, Aftab, Saeed, Shad Manaman, Navid
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2024
• Subjects: Poro-acoustic impedance; Porosity prediction; Reservoir characterization; Seismic attribute; Seismic inversion; Poro-acoustic impedance; Reservoir characterization; Seismic attribute; Seismic inversion; Porosity prediction
• DOI: 10.1016/j.jappgeo.2024.105351
• ISSN: 0926-9851

For the purpose of reservoir modelling, precise porosity estimation is vital as it directly influences the storage capacity, fluid flow dynamics, and overall productivity of the reservoir. The computation of porosity is a key component of reservoir characterization. The Poro-Acoustic Impedance (PAI), a seismic inversion attribute, has proven to be effective for porosity estimation in hydrocarbon reservoirs. PAI, an extended version of Acoustic Impedance (AI), incorporates porosity information directly, enhancing its utility in forward modelling and seismic data inversion. In this study, offshore oil resources in Iran were examined, focusing on two components: sandstone and carbonate. The results of AI and PAI were compared, indicating that PAI is a suitable attribute for estimating porosity. The correlation between porosity and AI was −45%, while it was −74% with PAI. Moreover, the synthetic seismogram created using PAI aligns more closely with real seismograms. Porosity was estimated using both AI and PAI, with a 72% correlation between the porosity estimated using AI and the actual porosity. However, the correlation increased to 78% when using PAI. Furthermore, the porosity section was calculated using both AI and PAI, concluding that the PAI porosity section aligns more closely with the porosity log and provides a greater contrast in low porosity zones compared to AI. Given that porosity is incorporated into the PAI formula, the PAI porosity section and inversion results can be used as an indicator for evaluating the hydrocarbon capacity of the reservoir. The findings of this research suggest that PAI is an effective attribute for porosity estimation, bridging the gap between seismic data and porosity estimation, thereby enhancing our understanding and exploration of the reservoir. •Porosity estimation using PAI has better accuracy and precision.•The porosity section obtained using PAI is more consistent with the porosity log.•The effect of porosity is directly involved in the inversion procedure.•The PAI formula is simple and effective to use.