A brief history of the development of geostatistical techniques in the oil industry
Published:January 01, 1998
The quantification of geology has always been a fascinating topic, and the first pioneering efforts to reach this goal were those of Vistelius (1949) and his many followers using Markov chain analysis to quantify one-dimensional lithological sequences along wells (Fig. 1). Many successes were encountered with this approach, but it appeared difficult to generalize to the second and third dimension. Then, in the mid Sixties, the giant Hassi-Messaoud field in Algeria was the object of pioneering applications of quantitative reservoir description techniques. The distribution of sand lenses and shale breaks was modelled in a vertical cross-section (see Fig. 2), with the goal of understanding their impact on effective permeability. Shales were represented as thin sheets, whilst sands were given a varying thickness and a constant width. These bodies were distributed randomly in the cross-section. Their lateral extent had been derived from a detailed outcrop study of the Ajjers’ Tassili analogue (Algeria). This model was used as a basis for reservoir simulation and it was observed that, because heterogeneities were modelled in a realistic way, a satisfactory history-match could be achieved more easily. An overview of the approach used is given in Delhomme and Giannesini, 1979. This Hassi-Messaoud application seems to have been rather isolated in the petroleum industry.
In the mining industry, things were different. G. Matheron, at the Centre de Géostatistique (France) , had pioneered the use of Mining Geostatistics in the early Sixties (Matheron, 1970, Journel and Huijbregts, 1978), and its use was spreading fast.
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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.