Estimated pay mapping using three‐dimensional seismic data and incremental pay thickness modeling

Abstract

Better estimates of hydrocarbon pay thickness and reservoir distribution are achieved if forward modeling is combined with crossplot cluster analysis before the seismic amplitude and isochron data are converted into estimates of pay thickness. To facilitate this process, an enhanced convolutional modeling technique that incorporates petrophysical data and equations into the synthetic seismogram generation process was developed. These incremental pay thickness (IPT) forward models provide the pertinent seismic and petrophysical values required for crossplot analysis. The crossplot analyses then define which seismic variables (trough amplitude, peak amplitude, time structure, isochron, etc.) are most uniquely related to a pay thickness parameter (gross thickness, net thickness, net porosity thickness, or hydrocarbons in place). Work to date, mostly in offshore Gulf Coast gas sands, has shown significant variation in the crossplot transforms required to convert seismic data to estimated pay maps. As such, an interactive, model‐based, interpretive approach is recommended as an appropriate means to integrate petrophysical, geologic, and 3-D seismic data in the creation of reservoir pay maps.