Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Abstract

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

Rock physics modelling and inversion for saturation‐pressure changes in time‐lapse seismic studies

“…Other inversion methods such as stochastic optimization algorithms could be applied using the same forward model in Landrø (). An example of stochastic inversion approach has been proposed in Veire, Borgos and Landrø (). Improvements to this formulation have been subsequently proposed by Meadows (), Trani et al .…”

Stochastic inversion of pressure and saturation changes from time‐lapse multi component data

“…Since shear waves do not propagate through fluids, combining shear‐wave seismic data with compressional seismic data might increase our ability to separate fluid‐ and pressure effects from seismic data. Veire et al . (2003, 2006) found a strong correlation between relative change in S‐wave velocities and change in effective pressure, indicating that combining pressure wave seismic data with converted‐wave seismic data might increase the quality of the results.…”

“…Buland and El Ouair (2006) and Veire et al (2006) use linear approximations to perform the Bayesian inversion. This makes the posterior probability distribution analytically tractable and exact expressions for the mean and covariances of the distributions can be found.…”

“…This methodology is more flexible than the linear approximations, but it requires use of Monte Carlo sampling to obtain estimates of the mean and covariance of the posterior distributions. The method presented by Veire et al (2006) and the method presented here can easily be extended to include non‐Gaussian distributions and non‐linear functions, at the cost of going to Monte Carlo sampling.…”

“…In the following, a stochastic model is presented for estimating fluid and pressure related changes directly from repeated multicomponent amplitude variation with offset (AVO) data and travel time changes through a Bayesian framework. The prior model is similar to the one presented in Veire et al . (2003, 2006), but a stochastic model for time‐lapse seismic travel time changes has also been included.…”