Review—SAS GGRE analytics for oil and gas

White paper outlines geology, geophysics and reservoir engineering usage of statistical package.

SAS’s Whitepaper ‘Geology, Geophysics and Reservoir Engineering (GGRE) Analytics for the Oil and Gas Industry’ is midway between a manual for SAS’ geostatistical package and an introductory text. Author Keith Holdaway, a geophysicist, now works with SAS’ R&D unit. Holdaway’s starting point is the need for proper integration of disciplines, data fusion, risk reduction and uncertainty management. Here ‘soft computing’ methods allow information from various sources with varying degrees of uncertainty to be integrated and mined for relationships between measurements and reservoir properties.

SAS Geostatistics, a component of SAS Analytics, provides answers to questions on risk and uncertainty, ‘endorses’ reserves information and ensures that exploitation plans are in line with targets. SAS Analytics offers predictive and descriptive modeling, forecasting and spatial analysis that incorporates variograms, kriging and simulation to better understand the reservoir.

Industry use cases include Total’s use of nonlinear regression to condition production data and Stingray Geophysical’s ‘GODS’ permanent reservoir monitoring system.

Perhaps Holdaway’s most contentious suggestion is that making access to such complex tools easy turns time-constrained geoscientsts into geostatistical experts. Can an ‘intuitive interface’ make up for a lack of a ‘specific skill set and a knowledge of the ‘nuances of statistical analysis?’ Whatever the answer, SAS geophysics offers a point-and-click interface that exposes data mining, text mining and visualization tools to expose GGRE data across disparate geoscience disciplines. Our main regret though is that, as a geophysicist, Holdaway did not make more of this opportunity to explain the relationship between the broadband approach of geostatistics and the band limited Fourier analysis. Geostatistics is more than a spatial equivalent of time series analysis. Download the SAS Whitepaper from (registration required).

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