We illustrate the estimation of the spatial distribution of porosity, saturation, and shaliness for two gas sand reservoirs, one hosted in a complex channel system and the other in a sequence of turbiditic sandstones, directly from the seismic observations. To this end, we employ seismic AVA inversion methods in which the quantitative relations between elastic and petrophysical properties are directly included in the forward modeling of the inversion kernel. These inversion approaches have recently become standard tools in reservoir characterization studies. We discuss a target-oriented method, where we consider the AVA response of the interface between the cap rock and the reservoir, and two interval-oriented approaches, where we invert the angle traces in a time interval that includes the reservoir layers. In solving the inverse problem, we make use of either analytical equations or numerical MCMC algorithms. All the methods are cast in a Bayesian framework so that we estimate both the most likely solutions and the associated uncertainties.
Applications of seismic AVA inversions for petrophysical characterization of subsurface targets
Mazzotti A.;Aleardi M.
2022-01-01
Abstract
We illustrate the estimation of the spatial distribution of porosity, saturation, and shaliness for two gas sand reservoirs, one hosted in a complex channel system and the other in a sequence of turbiditic sandstones, directly from the seismic observations. To this end, we employ seismic AVA inversion methods in which the quantitative relations between elastic and petrophysical properties are directly included in the forward modeling of the inversion kernel. These inversion approaches have recently become standard tools in reservoir characterization studies. We discuss a target-oriented method, where we consider the AVA response of the interface between the cap rock and the reservoir, and two interval-oriented approaches, where we invert the angle traces in a time interval that includes the reservoir layers. In solving the inverse problem, we make use of either analytical equations or numerical MCMC algorithms. All the methods are cast in a Bayesian framework so that we estimate both the most likely solutions and the associated uncertainties.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.