This paper focuses on integrating field-production history into reservoir characterization through stochastic inverse modeling. A key element of our approach is a three-dimensional (3D) streamline simulator that is orders of magnitude faster than traditional numerical simulators and thus allows for rapid inversion of multiphase production data. Stochastic permeability fields, conditioned to field-production history, are then generated with simulated annealing. We also explore the spatial resolution associated with estimates of reservoir permeability variations derived by use of field-production history. On the basis of techniques from geophysical inverse theory, we address such issues as data sensitivity, spatial resolution, averaging kernels, and uncertainties associated with our estimates of reservoir permeability. The proposed inversion technique has been applied to synthetic as well as field cases. The synthetic example involves a sensitivity analysis of multiphase production history in heterogeneous five-spot and nine-spot patterns. The field example consists of production history from a five-spot pattern in the north Robertson unit, a low-permeability carbonate reservoir in West Texas. Watercut history at the producers are used to estimate permeability variations in a two-layer (matrix-fracture) model of the reservoir. All computations were performed on a 125 MHz Pentium with an average run time of about 4 wall-clock hours, indicating the feasibility of our approach.