Novel approaches to uncertainty estimation in seismic subsurface characterization

Abstract

Uncertainty estimation in subsurface characterization workflows is an important input to decision-making in earth science-related problems. We present three methods to characterize seismic-related uncertainty, each of which includes a real data case study. Two of these methods are designed to characterize depth uncertainty in positioning of migrated reflectors. Such estimates may be used in derisking well depth prognoses, analysis of well misties, and for estimating ranges on resource volume. The first method derives rapid and robust estimates of depth uncertainty around an existing velocity model using traditional velocity analysis to constrain the solution space while limiting a-priori constraints, consistent with a frequentist approach to uncertainty characterization. The second method characterizes depth uncertainty more rigorously in respect to the physics and prior information available by performing full-waveform inversion in a Bayesian framework. This method produces similar uncertainty estimates to the first in the case of simple velocity models but is more accurate where the overburden is complex; however, it requires significantly greater computational expense, thus limiting current practical applications. The third method is an amplitude variation with angle (AVA) inversion for reservoir properties designed to output uncertainty products for interpreters, utilizing the Bayesian framework and Zoeppritz equations to define the forward physics. Application to an offshore Egypt field demonstrates that it can generate reliable estimates of reservoir properties (e.g., lithofluid type or shale volume) including uncertainty, useful in various parts of subsurface characterization. These results also show that the method could provide improved point estimates of reservoir properties compared to conventional deterministic AVA inversion approaches. There is usually a trade-off between increasing the accuracy of subsurface characterization versus creating faster, less expensive, and more readily understood workflows for practitioners. We discuss how the relative importance of these competing factors should be considered within the context of how the outputs will be used.