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Improved Resolution of Imaging the Subsurface via Nonlinear Signal Algorithms

Detection of oil underground can be done by a variety of methods, and seismic survey imaging, or seismology, is the most popular. Seismology for oilers is using wave energy that can nondestructively pass through rock layers and providing data about those rocks with the waves that are reflected back to the surface. Various types of rocks and substances like oil have different densities and therefore will look different in seismology data, or referred to as dispersion maps, provided by the measurement.

Interpreting the data produced by the reflected waves is important for accurately determining the presence of oil or other properties of the subterranean. Other methods use linear-type correlations to process this data and are therefore limited with regards to accuracy and especially for waves measured at low frequency. This technology is a method of waveform analysis and utilizes a non-linear approach to process the dispersion maps during seismology. It is proven to show much higher resolution over a wide range of high and low frequency. Better detection means higher returns for oil investments and extraction operations.

Applications

  • Well logging business in the oil and gas industry
  • Surface seismic imaging industry where surface waves are used in imaging
  • Industries related to geotechnical activities where surface waves are used to image shallow targets (e.g., construction, archeology, soil, ocean bottom for port construction)

Problems Addressed

  • Linear algorithms used for interpreting dispersion maps have poor resolution at lower frequencies which is important for detecting oil

Competitive Advantages

  • 10X increase in accuracy for prediction of rock formation type & fluid (if existing in area of measurement) during seismology experiments of the subterranean
  • Has shown verified proof of concept with industrially supplied data sets

Patents

  • WO 2017/223079

Publications

  • Zheng and Hu, Bulletin of Seismological Society of America, 2017