2013
DOI: 10.1111/1365-2478.12005
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Adaptive scaling for an enhanced dynamic interpretation of 4D seismic data

Abstract: A B S T R A C TIn this study, importance is drawn to the role of engineering principles when interpreting dynamic reservoir changes from 4D seismic data. In particular, it is found that in clastic reservoirs the principal parameters controlling mapped 4D signatures are not the pressure and saturation changes per se but these changes scaled by the corresponding thickness (or pore volume) of the reservoir volume that these effects occupy. For this reason, pressure and saturation changes cannot strictly be recove… Show more

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Cited by 19 publications
(8 citation statements)
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“…In our case, as we are dealing with relatively small changes, one obvious form for f(ΔR, G) can be obtained by a linear polynomial (Falahat et al 2013) (2)…”
Section: Methodsmentioning
confidence: 99%
See 3 more Smart Citations
“…In our case, as we are dealing with relatively small changes, one obvious form for f(ΔR, G) can be obtained by a linear polynomial (Falahat et al 2013) (2)…”
Section: Methodsmentioning
confidence: 99%
“…The coefficients are functions of the reservoir geology, rock properties and fluid properties. As the main reservoir variability is supplied by the baseline amplitude, the weighting coefficients a i can be assumed fixed across the reservoir (Falahat et al, 2013).…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…From the engineering perspective, these 4D signals contain crucial information for updating reservoir models. A number of specialized techniques have been developed to extract quantitative reservoir engineering information, especially pressure and saturation changes, from these 4D seismic signatures (Landrø, 2001;Mac-Beth et al, 2006;Falahat et al, 2013). After obtaining quantitative reservoir dynamic changes, workflows have also been proposed to close the loop between the observed and predicted 4D seismic and production history, to improve the reliability of reservoir simulation models for efficient well planning and production strategies (Staples et al, 2005;Landa and Kumar, 2011;Souza et al, 2011;Ayzenberg et al, 2013;Alerini et al, 2014;Ayzenberg and Liu, 2014;Tian et al, 2014).…”
Section: Introductionmentioning
confidence: 99%