Search

Fluvio-deltaic sedimentary systems are of great interest for explorationists because they can form prolific hydrocarbon plays. However, they are also among the most complex and heterogeneous ones encountered in the subsurface, and potential reservoir units are often close to or below seismic resolution. For seismic inversion, it is therefore important to integrate the seismic data with higher resolution constraints obtained from well logs, whereby not only the acoustic properties are used but also the detailed layering characteristics. We have applied two inversion approaches for poststack, time-migrated seismic data to a clinoform sequence in the North Sea. Both methods are recursive trace-based techniques that use well data as a priori constraints but differ in the way they incorporate structural information. One method uses a discrete layer model from the well that is propagated laterally along the clinoform layers, which are modeled as sigmoids. The second method uses a constant sampling rate from the well data and uses horizontal and vertical regularization parameters for lateral propagation. The first method has a low level of parameterization embedded in a geologic framework and is computationally fast. The second method has a much higher degree of parameterization but is flexible enough to detect deviations in the geologic settings of the reservoir; however, there is no explicit geologic significance and the method is computationally much less efficient. Forward seismic modeling of the two inversion results indicates a good match of both methods with the actual seismic data.

In order to retrieve a high-resolution reservoir model from seismic and well data, an approach was developed based on an a priori layered model from well data, specifically the acoustic impedances derived from the sonic and density logs. The procedure consists of using a forward model of the well data as a priori information that is then iteratively matched with the seismic data using a Bayesian inversion process. The inversion is then extended to 2D, whereby the extrapolation is guided by a simple geometric envelope described with a small number of parameters. It is tested on a seismic data set containing a deltaic clinoform in the North Sea, whereby the clinoform geometry is parameterized by a sigmoid and used as prior information. In the subsequent optimization the clinoform geometry is further refined with a limited number of local knots to improve the match with the seismic data. This lowparameterization inversion approach thus uses geological shapes and well constraints to obtain a subsurface model than can have a substantially higher resolution than the seismic wavelength.

Summary Many important details of potential subsurface reservoirs that we wish to characterize are only indirectly present in the reflected wavefields measured at the Earth’s surface. Therefore, the analysis of seismic data always presents an inversion problem. Instead of analyzing the data trace by trace, we propose an automated procedure that adjusts the parameters of a two-dimensional geological model by minimizing the mismatch between the simulated and measured seismic. This approach differs from standard inversion problems in that the size of the required details of the 2D geological reservoir model is far below the limits of the seismic resolution.