Combined inversion of 4D seismic waveform data and production data using ensemble Kalman filter

Abstract

In this computational study we combine 4D seismic data with production data to continuously update the reservoir model during production. The inversion is based on the ensemble Kalman filter (EnKF), and the forward method is a combination of a fast seismic modeling tool and a reservoir simulator. The EnKF method is a Monte Carlo approach, and state variables, as fluid saturations and pressure, and model input parameters as the porosity and permeability, are updated in the reservoir model at each assimilation step. The assimilated measurements are seismic waveforms data and production data, which are measured gas‐oil ratio, water cut and flowing bottomhole pressure. The updated models allow for improved estimation of the parameters and the state variables during the production, because additional well data and repeated seismic surveys are sequentially included in the inversion. The method is applied to a synthetic 2D reservoir model, and it is shown that introduction of production and seismic waveforms data gives a fairly good estimation of the porosity and permeability fields.