Despite long production histories, operators of mature oilfields sometimes struggle to account for reservoir compartmentalization. Geological-led workflows do not adequately honor legacy production data since inherent bias is introduced into the process of allocating production by interpreted flow units. This paper details the application of machine learning methods to identify possible reservoir compartments based on legacy production data recorded from individual well completions. We propose an experimental data-driven workflow to rapidly generate multiple scenarios of connected volumes in the subsurface. The workflow is premised upon the logic that well completions draining the same connected reservoir space can exhibit similar production characteristics (rate declines, GOR trends and pressures). We show how the specific challenges of digitized legacy data are solved using outlier detection for error checking and Kalman smoothing imputation for missing data in the structural time series model. Finally, we compare the subsurface grouping of completions obtained by applying unsupervised pattern recognition with Hierarchal clustering. Application of this workflow results in multiple possible scenarios for defining reservoir compartments based on production data trends only. The method is powerful in that, it provides interpretations that are independent of subsurface scenarios generated by more traditional workflows. We demonstrate the potential to integrate interpretations generated from more conventional workflows to increase the robustness of the overall subsurface model. We have leveraged the power of machine learning methods to classify more than forty (40) well completions into discrete reservoir compartments using production characteristics only. This effort would be extremely difficult, or otherwise unreliable given the inherent limitations of human spatial, temporal, and cognitive abilities.

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