Quantification of geology
Published:January 01, 1998
A key problem faced in the development of a hydrocarbon reservoir is that of constructing a reservoir model that can generate reliable production forecasts under various development scenarios. After a few appraisal wells have been drilled, or after a few years of production, the reservoir geologist will provide a model of the inter-well geological architecture. This model (see example in Fig. 10) may be a conceptual representation of the architecture of genetic bodies (e.g. fluvial channels, floodplain shales) within which petrophysical variations can later be distributed. Such representations can have a very important impact on economical decisions. Examples are the location of an in-fill well, or a reservoir simulation exercise, the results of which influence the choice of a development strategy.
Correlation panels such as those shown in Fig. 10 are a representation of the subsurface that incorporates the well data and the interpretation of the depositional environment, facies associations and geometries. It is well known that if geological models are to be used for reservoir simulation, and hence as a basis for development decisions, they must be generated in three dimensions. Two-dimensional models do not provide a realistic representation of reservoir connectivity. Unfortunately, manual construction of 3-D geological models is close to impossible, which explains why geologists often limit their interpretations to 2-D correlation panels, fence-diagrams or maps. As a result of this, there is often a gap between conceptual geological representations and models used in reservoir simulations because the gridded 3-D model used by reservoir engineers does not incorporate the geological knowledge embedded in detailed 2-D correlation panels.
Figures & Tables
Geostatistics In Petroleum Geology
This publication comes from a course designed to explain, in non-mathematical terms, that geostatistics is a simple and flexible formalism for quantifying geology. Topics included in the publication are a brief history of the development of eostatistical techniques in the oil industry, the use of geostatistics for multidisciplinary data integration, andthe use of geostatistics for quantification of undertainty.