Geophysics-Based Fluid-Facies Predictions Using Ensemble Updating of Binary State Vectors

Fluid flow simulations are commonly used to predict the fluid displacement in subsurface reservoirs; however, model validation is challenging due to the lack of direct measurements. Geophysical data can be used to monitor the displacement of the fluid front. The updating of the fluid front location in two-phase flow problems based on time-lapse geophysical data can be formulated as an inverse problem, specifically a data assimilation problem, where the state is a vector of binary variables representing the fluid-facies and the observations are measurements of continuous geophysical properties, such as electrical or elastic properties. In geosciences, many data assimilation problems are solved using ensemble-based methods relying on the Kalman filter approach. However, for discrete variables, such approaches cannot be applied due to the Gaussian-linear assumption. An innovative approach for mixed discrete-continuous problems based on ensemble updating of binary state vectors is presented for fluid-facies prediction problems with time-lapse geophysical properties. The proposed inversion method is demonstrated in a synthetic two-dimensional simulation example where water is injected into a reservoir and hydrocarbon is produced. Resistivity values obtained from controlled-source electromagnetic data are assumed to be available at different times. According to the results, the proposed inversion method is to a large extent able to reproduce the true underlying binary field of fluid-facies.

Automated summary

Fluid flow simulations are commonly used to predict fluid displacement in subsurface reservoirs, but model validation is challenging due to the lack of direct measurements. Geophysical data can be used to monitor fluid displacement, and ensemble-based methods can help solve fluid-facies prediction problems with time-lapse geophysical data.