DOI: 10.1190/tle-2026-1098 ISSN: 1070-485X

Distance and quadrant seismic attributes for reservoir characterization across diverse geologic and AVO settings

Noor ul huda Choudhry, Heather Bedle, Dennis B. Neff, Warren Neff

Abstract

Distance and Quadrant (DQ) trace attributes provide an integrated AVO-based framework for reservoir characterization, structural interpretation, and stratigraphic analysis. Although the DQ and ThetaPX attributes have been introduced in earlier work using synthetic traces, this paper presents the first comprehensive demonstration of the full ten-attribute DQ suite across four contrasting geologic and AVO settings: an onshore thin gas sand (Colony Formation, Alberta, Canada), an offshore CO2 storage reservoir (Sleipner field, Norway), a Class 4 AVO gas field in a faulted fluvial deltaic system (Kupe field, New Zealand), and a Class 1 mixed carbonate clastic multilithology play (Grayback SE field, Texas). A single DQ processing workflow generates all ten attribute volumes including DQ, ThetaPX, StickOgram, signed isochron, signed half isochron, Average DQ, Sum DQ, Median ThetaPX, and optimized near and far-minus-near stacks generated directly from migrated near- and far-angle stack volumes, without prior well calibration or wavelet estimation. The workflow embeds automated interpretation elements that organize amplitude and offset information into attribute volumes suitable for advanced seismic analysis and machine learning applications. These attributes support three-dimensional visualization, horizon tracking, fault interpretation, stratigraphic facies classification, well ties, layer thickness estimation, and AVO crossplot analysis. By demonstrating consistent attribute behavior across bright-spot, dim-spot, Class 3, Class 4, and CO2 saturation settings, this study establishes the DQ workflow as a broadly applicable preliminary screening tool prior to quantitative inversion or machine learning analysis. Results from this screening process can help focus and guide subsequent reservoir characterization efforts.

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