Timelapse by the numbers: elastic modeling of repeatability issues

David C. Henley and Joe Wong and Peter M. Manning


An important emerging application for seismic reflection imaging is the remote monitoring of hydrocarbon production from a formation, or the injection of a fluid like CO2 for sequestration underground. In this application, it is important for seismic data acquisition and processing to be reliably repeated at regular intervals, over a period of time sufficient to provide a ‘difference anomaly’ history of the monitored process. One way to explore the detectability of this anomaly is to model the time-lapse process numerically. Since elastic modeling is probably the most realistic way to simulate the earth response to a seismic survey, a state-of-the-art elastic modeling program was used to generate seismic surveys corresponding to several ‘baseline’ and corresponding ‘time-lapse’ 2D earth models. Each time-lapse model differed from its baseline only in a small subsurface zone, where properties were altered to simulate fluid exchange. This work explored the detectability of the time-lapse anomaly relative to various acquisition and processing parameters. With identical acquisition parameters for a baseline model and its matching time-lapse model, the detectability of the anomaly was surprisingly robust in the presence of both random and coherent noise. In the presence of significant simulated ‘seasonal’ statics variations, the anomaly remained detectable, with suitable processing. This study is a partial demonstration of the realistic modeling software available at CREWES, and the kinds of phenomena that can be usefully modeled.

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