Inversion of seismic data using information from horizontal wells is often hampered by cumulative well-location errors. These errors can have a significant influence on the final subsurface model derived from the data. To achieve a proper data integration and arrive at correct uncertainty estimates, we formulate the problem in a fully probabilistic framework and present a numerical approach for improving subsurface imaging using uncertain well-log data and their uncertain locations as well as uncertain seismic data. The result is improved model error quantification in the seismic inversion process.