Transcripción del resumen del autor. Many equiprobable solutions exist while history matching a reservoir's performance, given the ill-posed nature of the inverse problem. To mitigate some of the uncertainty issues stemming from the initial static reservoir description, this study shows how continuous learning evolves when a slate of analytical tools are used while interpreting real-time surveillance data. The combined approach involving the use of analytical tools in conjunction with numerical simulations helps understanding reservoir performance, which, in turn, allows insights into history matching. Specifically, we demonstrate the use of various analytical tools to learn about (1) time-dependent behavior of both producers and injectors with rate-transient analysis to assess an evolving waterflood, (2) reservoir heterogeneity with pressure-transient analysis, (3) degrees of time-variant injection support with the reciprocal-productivity index, (4) reservoir dynamic connectivity with the capacitance-resistance model, and (5) real-time injection-well behavior with the modified-Hall analysis. The benefits of collective use of analytic tools demonstrate that they should be used either simultaneously or preferably before undertaking a detailed numeric flow-simulation study, particularly where real-time data are being gathered. In particular, the lack of performance match for the entire history with a numerical model becomes transparent when the learning from analytical tools is juxtaposed. This understanding paves the way for much improved learning of reservoir plumbing in a dynamic sense.

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