Southeast Lea Depth Reimaging and Survey Merge

1,000 mi2 of reimaged seismic data in the Permian Basin

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Reimaged legacy seismic surveys in the Permian Basin

WesternGeco has now completed the reimaging of 1,000 mi2 of 12 legacy surveys in Lea County, New Mexico, and Andrews and Winkler Counties, Texas. The newly merged surveys straddle the northern Delaware Basin, Central Basin Platform, and Northwest Shelf areas, each posing imaging challenges. The final prestack time migration and prestack depth migration products have superior broadband resolution and preservation of relative amplitude variation and accurate structure. This industry-funded project significantly enhances the legacy data, providing a regional geological representation of the structurally complex Permian Basin.

Blocks showing Southeast Lea Depth Reimaging and Survey Merge project
Legacy seismic data from Southeast Lea Depth Reimaging and Survey Merge project Reprocessed legacy seismic data from the Southeast Lea Depth Reimaging and Survey Merge project
Legacy data (left) before reprocessing and the final Kirchhoff prestack depth migration image showing improved resolution of geologic structure.

Advanced seismic imaging workflow delivers structurally enhanced depth image

Advanced imaging workflows employed multiple iterations of tilted transverse isotropy (TTI) using more than 50 wells as input to tomography modeling, resulting in a structurally enhanced depth image. True 5D matching pursuit Fourier interpolation (MPFI) was utilized to address limitations with different acquisition geometries to produce a seamlessly merged volume. The latest noise attenuation techniques—including coordinate-driven coherent noise attenuation techniques such as SWAMI surface wave analysis, modeling, and inversion—addressed significant noise issues, particularly in the Cenozoic fill zones. Surface and interbed multiple attenuation techniques—3D general surface multiple prediction (GSMP) and extended internal multiple prediction (XIMP)—were both crucial to producing a high-quality dataset.