Abstract

Summary The propagation of elastic waves in rocks is determined by the bulk modulus, shear modulus, and bulk density of the rock. In porous rocks all these properties are affected by the distribution of pore space, the geometry and interconnectivity of the pores, and the nature of the fluid occupying the pore space. In addition, the bulk and shear moduli are also affected by the effective pressure, which is equivalent to the difference between the confining (or lithostatic) pressure and pore pressure. During production of hydrocarbons from a reservoir, the movement of fluids and changes in pore pressure may contribute to a significant change in the elastic moduli and bulk density of the reservoir rocks. This phenomenon is the basis for reservoir monitoring by repeated seismic (or time-lapse) surveys whereby the difference in seismic response during the lifetime of the field can be directly related to changes in the pore fluids and/or pore pressure. Under suitable conditions, these changes in the reservoir during production can be quantitatively estimated by appropriate repeat three-dimensional (3D) seismic surveys which can contribute to understanding of the reservoir model away from the wells. The benefit to reservoir management is a better flow model which incorporates the information derived from the seismic data. What are suitable conditions? There are two primary factors which determine whether the reservoir changes we wish to observe will be detectable in the seismic data: the magnitude of the change in the elastic moduli (and bulk density) of the reservoir rocks as a result of fluid displacement, pressure changes, etc.; the magnitude of the repeatability errors between time-lapse seismic surveys. This includes errors associated with seismic data collection, ambient noise and data processing. The first is the signal component and the second the noise component. Previous reviews of seismic monitoring suggest that for 3D seismic surveys a signal-to-noise (S/N) ratio of 1.0 is sufficient for qualitative estimation of reservoir changes. Higher S/N ratios may allow quantitative estimates. After a brief examination of the rock physics affecting the seismic signal, we examine the second factor, repeatability errors, and use a synthetic seismic model to illustrate some of the factors which contribute to repeatability error. We also use two land 3D surveys over a Middle East carbonate reservoir to illustrate seismic repeatability. The study finds that repeatability errors, while always larger than desired, are generally within limits which will allow production-induced changes in seismic reflectivity to be confidently detected.

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