We use an extended elastic impedance (EEI) inversion for quantitative reservoir characterization. The EEI approach is applied to both on-shore and off-shore seismic data where target reservoirs are gas-bearing sands located in sand-shale sequences. The workflow we adopt can be divided into three phases. The starting point is a petrophysical analysis in which the relationships between petrophysical and elastic properties are analyzed. The second step of EEI analysis uses a cross-correlation procedure to determine the best chi (χ) projection angles for the petrophysical parameters of interest (i.e. porosity, water saturation and shaliness). In the final step, pre-stack seismic data are simultaneously inverted into P-wave velocity, acoustic, and gradient impedances, and the last two elastic volumes are finally projected to χ angles corresponding to the target petrophysical parameters. The estimated porosity, water saturation, and shaliness values reveal a proper match at blind well locations. This work shows that EEI is an effective tool for lithology and fluid prediction in clastic reservoirs. The output of this work can be beneficial for static reservoir model building and volumetric calculation and can be also used to determine new potential drilling locations.

Estimating petrophysical reservoir properties through extended elastic impedance inversion: applications to off-shore and on-shore reflection seismic data

M. Aleardi
Primo
2018-01-01

Abstract

We use an extended elastic impedance (EEI) inversion for quantitative reservoir characterization. The EEI approach is applied to both on-shore and off-shore seismic data where target reservoirs are gas-bearing sands located in sand-shale sequences. The workflow we adopt can be divided into three phases. The starting point is a petrophysical analysis in which the relationships between petrophysical and elastic properties are analyzed. The second step of EEI analysis uses a cross-correlation procedure to determine the best chi (χ) projection angles for the petrophysical parameters of interest (i.e. porosity, water saturation and shaliness). In the final step, pre-stack seismic data are simultaneously inverted into P-wave velocity, acoustic, and gradient impedances, and the last two elastic volumes are finally projected to χ angles corresponding to the target petrophysical parameters. The estimated porosity, water saturation, and shaliness values reveal a proper match at blind well locations. This work shows that EEI is an effective tool for lithology and fluid prediction in clastic reservoirs. The output of this work can be beneficial for static reservoir model building and volumetric calculation and can be also used to determine new potential drilling locations.
2018
Aleardi, M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/924222
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