The oil & gas industry has been the backbone of the world's economy in the last century and will continue to be in the decades to come. With increasing demand and conventional reservoirs depleting, new oil industry projects have become more complex and expensive, operating in areas that were previously considered impossible and uneconomical. Therefore, good reservoir management is key for the economical success of complex projects requiring the incorporation of reliable uncertainty estimates for reliable production forecasts and optimizing reservoir exploitation. Reservoir history matching has played here a key role incorporating production, seismic, electromagnetic and logging data for forecasting the development of reservoirs and its depletion. With the advances in the last decade, electromagnetic techniques, such as crosswell electromagnetic tomography, have enabled engineers to more precisely map the reservoirs and understand their evolution. Incorporating the large amount of data efficiently and reducing uncertainty in the forecasts has been one of the key challenges for reservoir management. Computing the conductivity distribution for the field for adjusting parameters in the forecasting process via solving the inverse problem has been a challenge, due to the strong ill-posedness of the inversion problem and the extensive manual calibration required, making it impossible to be included into an efficient reservoir history matching forecasting algorithm. In the presented research, we have developed a novel Finite Difference Time Domain (FDTD) based method for incorporating electromagnetic data directly into the reservoir simulator. Based on an extended Archie relationship, EM simulations are performed for both forecasted and Porosity-Saturation retrieved conductivity parameters being incorporated directly into an update step for the reservoir parameters. This novel direct update method has significant advantages such as that it overcomes the expensive and ill-conditioned inversion process, applicable to arbitrary reservoir geometries and enables efficient integration with real field crosswell EM data.