Joint inversion for mapping subsurface hydrological parameters

Accurately estimating reservoir parameters from geophysical data is vitally important in hydrocarbon exploration and production. We have developed a new joint-inversion algorithm to estimate reservoir parameters directly, using both seismic amplitude variation with angle of incidence ͑AVA͒ data and marine controlled-source electromagnetic ͑CSEM͒ data. Reservoir parameters are linked to geophysical parameters through a rock-properties model. Errors in the parameters of the rock-properties model introduce errors of comparable size in the reservoir-parameter estimates produced by joint inversion. Tests of joint inversion on synthetic 1D models demonstrate improved fluid saturation and porosity estimates for joint AVA-CSEM data inversion ͑compared with estimates from AVA or CSEM inversion alone͒. A comparison of inversions of AVA data, CSEM data, and joint AVA-CSEM data over the North Sea Troll field, at a location for which we have well control, shows that the joint inversion produces estimates of gas saturation, oil saturation, and porosity that are closest ͑as measured by the rms difference, the L 1 norm of the difference, and net values over the interval͒ to the logged values. However, CSEM-only inversion provides the closest estimates of water saturation.

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Reservoir-parameter identification using minimum relative entropy-based Bayesian inversion of seismic AVA and marine CSEM data

Hou

Rubin

Hoversten

et al. 2006

GEOPHYSICS

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A stochastic joint-inversion approach for estimating reservoir-fluid saturations and porosity is proposed. The approach couples seismic amplitude variation with angle (AVA) and marine controlled-source electromagnetic (CSEM) forward models into a Bayesian framework, which allows for integration of complementary information. To obtain minimally subjective prior probabilities required for the Bayesian approach, the principle of minimum relative entropy (MRE) is employed. Instead of single-value estimates provided by deterministic methods, the approach gives a probability distribution for any unknown parameter of interest, such as reservoir-fluid saturations or porosity at various locations. The distribution means, modes, and confidence intervals can be calculated, providing a more complete understanding of the uncertainty in the parameter estimates. The approach is demonstrated using synthetic and field data sets. Results show that joint inversion using seismic and EM data gives better estimates of reservoir parameters than estimates from either geophysical data set used in isolation. Moreover, a more informative prior leads to much narrower predictive intervals of the target parameters, with mean values of the posterior distributions closer to logged values.

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Joint inversion for mapping subsurface hydrological parameters

Cited by 3 publications

References 6 publications

MRE‐based bayesian inversion of seismic and EM data for identification of reservoir parameters

MRE‐based bayesian inversion of seismic and EM data for identification of reservoir parameters

Direct reservoir parameter estimation using joint inversion of marine seismic AVA and CSEM data

Reservoir-parameter identification using minimum relative entropy-based Bayesian inversion of seismic AVA and marine CSEM data

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