GLOCAL: A global-local optimization template for multiple history-matched reservoir parameters

Abstract

Parameterization of petroleum reservoirs is an important component of reservoir management. However, the process of reservoir parameterization is fraught with nonuniqueness of estimated parameters. Such nonuniqueness leads to large uncertainty and risks in reservoir management. One way to quantify and reduce the uncertainty in parameter estimation is to generate multiple realizations of the parameter field often through geostatistical simulations. Such realizations do not reproduce the production history and there is always a need to adjust such models to match the history data. Conditioning all geostatistically-simulated reservoir models to production data is often time-consuming, and the process does not usually produce maps that truly mimic the actual reservoir heterogeneity.This work presents a global-local optimization template (GLOCAL) that produces multiple estimates of the parameter field by history-matching production data. More fundamentally, the technique produces realizations of the parameter field at different scales of resolution. Two sample history-matching problems are presented to illustrate the effectiveness of the method. Results from these examples show that the method can produce several estimates that show some pattern of heterogeneity found in the true field. However, the resemblance to the true field varies across different scales of resolution of the parameter field in the global template.