An ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As an advanced technology in reservoir surveillance, crosswell EM tomography can be used to estimate a cross-sectional conductivity map and associated saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this information into reservoir simulation in combination with other available observations is expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty.

The proposed approach applies ensemble-based data-assimilation methods to build a robust and flexible framework in which various sources of available measurements can be integrated. A comparative study evaluates two different implementations of the assimilation of crosswell EM data. The first approach integrates the crosswell EM field components in their original form, which entails forward simulation of the observed EM responses from the simulated reservoir state. In the second approach, formation conductivity is derived from the EM data through inversion and is subsequently assimilated into the reservoir model. An image-oriented distance parameterization of the fluid front assimilates the conductivity field in an efficient and robust manner and overcomes issues with data size, errors, and their correlation. Numerical experiments for different test cases with increasing complexity provide insight into the performance of the two proposed integration schemes. The results demonstrate the efficiency of the developed history-matching workflow and the added value of crosswell EM data in enhancing the reservoir characterization and reliability of dynamic reservoir forecasts.