Abstract Carbonates are known to be heterogeneous. In this paper, we will focus on characterising inter-well connectivity by applying a multi-dimensional approach to production data analysis along with integration of inter-disciplinary data for a Brazillian carbonate reservoir. Results of the analysis are used for interpretation of a noisy 4D seismic data to locate sweet spots. This unique integrated approach characterizes inter-well connectivity from four perspectives: (1) determine reservoir quality (2) identify source of water production (3) tracking of injected fluid's flow path (4) verify impact on 4D seismic response. We will show how the quality of a carbonate reservoir and its aquifer strength can be verified with well logs, pressure depletion rates and production behaviour. The use of sensitivity analysis in mechanistic models will also be shared to analyse the impact of heterogeneity on production behaviour. Using Chan's (1995) water-oil-ratio diagnostic plots, the source of water production will be identified as well. Explanation of analysis of well chronology, bubble maps of water cut will also be provided for tracking of injected fluid flow paths. Finally, interaction of production parameters between well-pairs and resistance modelling will be used to evaluate inter-well connectivity, and verified with 4D seismic data. Findings from all the analysis in the integrated approach are summarized into an inter-well connectivity metric, which is used as a reference for production and seismic history matching and interpretation of the noisy 4D seismic data. The integrated data analysis shows that the sweet spot corresponds with softening on the 4D seismic map, un-swept by injectors as it is located on a structural high southeast of the reservoir. This paper offers a comprehensive analysis to characterize important reservoir characteristics (such as thief zones and tight streaks). It will also emphasize ways to integrate inter-disciplinary data, and showcase various visualization perspectives to fortify and enhance the importance of data integration.