44th Annual Golf Tournament (22 Sep 2026)

Tue, 22 Sep 2026 12:00:00 GMT

To register, please email dan.bafia@clr.com

October Technical Meeting: Noor ul Huda Choudhry (OU) (26 Oct 2026)

Mon, 26 Oct 2026 16:30:00 GMT

SHORT BIOGRAPHY

Noor ul huda Choudhry is a PhD student in Geophysics at the University of Oklahoma's School of Geosciences, working in the AASPI Lab under Dr. Heather Bedle. Her research focuses on Distance and Quadrant (DQ) seismic attributes, applied to reservoir characterization, fault detection, and CO2 storage monitoring. Before returning to academia, she worked as a Reservoir Geoscientist at LMKR. She serves as Treasurer of the SEG OU Student Chapter, and received the Ben Hare Excellence in Geophysics Award recognizing her research.

PRESENTATION TITLE

Reading Between the Lines: What Polar Coordinates Reveal in AVO Data That Crossplots Hide

ABSTRACT

Class 1 AVO responses are some of the hardest in seismic interpretation. Weak reflectivity contrasts push brine sand, shale, and hydrocarbon-bearing sand into the same crowded region of an intercept-gradient crossplot, and separating them often comes down to interpreter judgment rather than the data itself. This problem shows up directly in the Volve Field in the North Sea, where standard AVO analysis cannot cleanly resolve fluid from lithology, and a known DVp/Vs flat spot in the Hugin Formation sits right at the edge of what conventional amplitude volumes can show.

The Distance and Quadrant (DQ) transform takes a different approach: instead of plotting intercept against gradient in Cartesian space, it re-expresses that same information in polar coordinates, separating how strong a reflection is from what direction it points, without altering the original amplitude spectrum. Run directly on migrated near and far stacks, with no well ties or wavelet estimation required, the workflow produces ten attributes, including DQ, ThetaPX, and signed isochron, that sharpen fault and channel edges, expose overburden structure invisible in conventional amplitude data, and bring that flat spot into clearer view.

This talk walks through the Volve results in detail, then shows the same workflow holding up across three very different settings: a thin gas sand in Alberta, a CO2 storage monitoring survey at Sleipner, and a faulted Class 4 gas field in New Zealand. It closes by discussing why attributes built on physical grounding rather than statistical fitting make a more trustworthy screening step ahead of machine learning or full inversion.

Geophysical Society of Oklahoma City upcoming events

44th Annual Golf Tournament (22 Sep 2026)

October Technical Meeting: Noor ul Huda Choudhry (OU) (26 Oct 2026)