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ABSTRACT Uncertainty typically reduces as data are acquired throughout a field's lifecycle. However, it remains significantly high during the early stages of exploration, particularly in geologically complex, multi‐pinnacle carbonate structures. This study aims to optimize appraisal strategies by maximizing the value of information (VOI) derived from large‐scale well testing in a deepwater exploration well. Given the high cost of data acquisition in deepwater—and especially ultra‐deepwater—settings, it is critical to fully leverage existing drill stem test (DST) data. The analysis focuses on evaluating vertical and lateral reservoir connectivity to support more informed decision‐making in early‐phase exploration. An ensemble of realizations was generated using experimental design to capture key uncertainty parameters. Artificial intelligence and machine learning (AI/ML) techniques, including neural networks and random forest algorithms, were applied to identify the optimal model configuration that best matches the well observed test pressure data. Simulated pressure responses from the ensemble were post‐processed to derive pressure derivative curves and assess the diagnostic quality of each scenario. Several AI/ML iterations were conducted to achieve convergence and refine the model toward an optimized match with the actual well test data. The initial ensemble results indicated that introducing lateral heterogeneity significantly improved the model's ability to replicate pressure derivative trends, aligning with analytical pressure transient analysis that suggested permeability degradation away from the wellbore. A second batch of ensemble realizations further enhanced the match quality, though refinements were still required. By the third iteration, the model achieved a high‐quality history match. Subsequently, an AI/ML‐driven proxy model—trained on all ensemble outputs—was employed to fine‐tune key well parameters such as skin and productivity index. The final matched models confirmed lateral connectivity between pinnacle features using data from a single well test, reducing the need for additional appraisal wells in this ultra‐deepwater setting where well costs are exceptionally high. Vertical connectivity was also validated through the ensemble analysis; only models incorporating vertical communication successfully reproduced the characteristic pressure derivative observed in the DST. This approach demonstrates the feasibility of leveraging high‐resolution well test data to optimize appraisal strategies and de‐risk future development in complex, high‐cost offshore environments. Conventional well test analysis is typically conducted using isolated analytical models or integrated into dynamic models by sensitizing near‐wellbore grids to match pressure derivative responses. This study introduces a novel approach by generating an ensemble of dynamic models guided by AI/ML techniques to achieve pressure match convergence while preserving structural integrity and honoring the geological and reservoir configuration. This workflow demonstrates an advanced integration of ML with ensemble‐based simulation to extract maximum value from a single well test in a complex, high‐cost offshore setting.