Christian Schiott

Kriging interpolation in seismic attribute space applied to the South Arne Field, North Sea

Seismic attributes can be used to guide interpolation in-between and extrapolation away from well log locations using for example linear regression, neural networks, and kriging. Kriging-based estimation methods (and most other types of interpolation/extrapolation techniques) are intimately linked to distances in physical space: If two observations are located close to one another, the implicit assumption is that they are highly correlated. This may, however, not be a correct assumption as the two locations can be situated in very different geological settings. An alternative approach to the traditional kriging implementation is suggested that frees the interpolation from the restriction of the physical space. The method is a fundamentally different application of the original kriging formulation where a model of spatialvariability is replaced by a model of variability in an attribute space. To the extent that subsurface geology can be described by a set of seismic attributes, we present an automated mult...

Anisotropic Velocity Changes In Seismic Time-lapse Data

A fundamental challenge in the interpretation of time shifts observed in time-lapse data is the decomposition of the time delay into a spatial compaction component and a velocity change component. Several authors (Hatchell and Borne, 2005; Janssen et al., 2006) have published the application of pragmatic linear relationships between overburden stretching and velocity changes which, have proved applicable in a wide range of geological settings.

Applying time-lapse seismic methods to reservoir management and field development planning at South Arne, Danish North Sea

Abstract At South Arne a highly repeatable time-lapse seismic survey (normalized root-mean-square error or NRMS of less than 0.1) allowed us to reliably monitor reservoir production processes during five years of reservoir depletion. Time-lapse AVO (amplitude v. offset) inversion and rock-physics analysis enables accurate monitoring of fluid pathways. On the crest of the field, water injection results in a heterogeneous sweep of the reservoir, whereby the majority of the injected water intrudes into a highly porous body. This is in contrast to a pre-existing reservoir simulation model predicting a homogeneous sweep. On the SW flank, time-lapse AVO inversion to changes in water saturation Δ S w reveals that the drainage pattern is fault controlled. Time-lapse seismic data furthermore explain the lack of production from the far end of a horizontal producer (as observed by production logging), by showing that the injected water does not result in the expected pressure support. On the highly porous crest of the reservoir compaction occurs. Time-lapse time shifts in the overburden are used as a measure for compaction and are compared with predictions of reservoir compaction from reservoir geomechanical modelling. In areas where compaction observations and predictions disagree, time-lapse seismic data give the necessary insight to validate, calibrate and update the reservoir geomechanical model. The information contained in time-lapse seismic data can only be fully extracted and used when the reservoir simulation model, the reservoir geomechanical model and the time-lapse seismic inversion models are co-visualized and available in the same software application with one set of coordinates. This allows for easy and reliable investigation of reservoir depletion and gives deeper insight than using reservoir simulation or time-lapse seismic individually.

Subsurface property interpolation in multi attribute space ‐ Porosities on the South Arne Field.

Kriging can be used as an interpolation technique to estimate the value of a parameter at unsampled locations. The interpolation depends on the existence of spatial dependency in the space where the parameter has been sampled. This space can be of any finite dimensional size, but is typically restricted to the 3D spatial (XYZ) or 4D spatio-temporal (XYZ-time) space. Here we will use kriging to interpolate between observed continuous data values in multivariate attribute space. We will show how we make use of this approach to perform kriging interpolation in a model parameter space spanned by seismic attributes. To the extent that the seismic attributes reflects the underground geology the interpolation will be based on similarity of geology rather than distance in spatial space. In an example from a chalk section of the South Arne Field in the North Sea, we use the method to estimate the distribution of porosity, using seismic attributes as guide. The results are very encouraging as the estimated maps follows known geological features very well. The porosity estimate is supported by subsequent drilling.