- Copyright © 2001 Society of Exploration Geophysicists
Turbidite reservoirs, deepwater clastic systems often characterized by complex sand distribution, stretch the limits of conventional seismic and geostatistical modeling and analysis. Nevertheless, such reservoirs must produce at a high rate to cover large drilling and production costs. Hence, reservoir heterogeneity must be accurately quantified and the associated uncertainty measured to determine the investment risk.
We propose a methodology to create fine-scale reservoir models of turbidite systems constrained by prestack seismic and well-log data and then apply it to a North Sea field in the preproduction phase. The first stage, a probability model of observing sandy and shaly facies (originally presented by Avseth) is based solely on field data. The original contribution of this paper is in the second phase when this coarse-scale seismic-derived probability map is integrated with smaller-scale variations of submarine channels using a new geostatistical method. The second step is needed because (1) seismic cannot detect individual smaller-scale channels which may act as conduits of fluids at depths of 2 km; (2) the seismic model is not locally constrained by small-scale well-log data; and (3) although the seismic-derived facies probability model provides uncertainty on the absence or presence of facies, it does not provide uncertainty about future cumulative oil production. The geostatistical approach proceeds first by simulating a reservoir training image using a Boolean simulation algorithm for channels. This training image is not constrained by any reservoir-specific data; it is merely conceptual. Next, a pixel-based geostatistical simulation method uses this training image to constrain alternative facies models (grid blocks of 12.5 × 12.5 × 1 m) to the seismic-derived facies probability, the small-scale well data, and the geometric patterns of channels as depicted by the training image. We show that our methodology is fast, general, and integrates all available data at relevant scales.
Modeling facies probability distributions from seismic
Glitne Field is a …