We present an efficient history matching approach that simultaneously integrates 4-D repeat seismic surveys with well production data. This approach is particularly well-suited for the calibration of the reservoir properties of high-resolution geologic models because the seismic data is areally dense but sparse in time, while the production data is continuous in time but averaged over interwell spacing. The joint history matching is performed using streamline-based sensitivities derived from either finite-difference or streamline-based flow simulation. Previous approaches to seismic data integration have mostly incorporated saturation effects but the pressure effects have largely been ignored. We propose, for the first time, streamline-based analytic approaches to compute parameter sensitivities that relate the seismic data to reservoir properties while accounting for both pressure and saturation effects via appropriate rock physics models. The inverted seismic data (e.g., changes in acoustic impedance or fluid saturations), is distributed as a 3-D high-resolution grid cell property or as a vertically integrated two-dimensional map. We derive pressure drop sensitivities along streamlines in addition to our previous work of water saturation sensitivity computation. The novelty of the method lies in the analytic sensitivity computations which make it computationally efficient for high resolution geologic models. We demonstrate the versatility of our approach by implementing it in a finite difference simulator which incorporates detailed physical processes, while the streamline trajectories provide for rapid evaluation of the sensitivities. The efficacy of our proposed approach is demonstrated with both synthetic and field applications. The synthetic example is the SPE benchmark Brugge field case. The field example involves waterflooding of a North Sea reservoir with multiple seismic surveys. For both the synthetic and the field cases, the advantages of incorporating the time-lapse variations are clearly demonstrated through improved estimation of the permeability distribution, pressure profile, and fluid saturation evolution and swept volumes.