2010
DOI: 10.1190/1.3485765
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The horizon cube: A step change in seismic interpretation!

Abstract: Conventional interpretation workflows require a limited number of key horizons to be mapped. These are used to construct a generalized geologic model on the basis of which economic decisions are made. In the process, gigabytes of data are reduced to just a few kilobytes of interpreted data, during which potentially valuable information is often lost.

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Cited by 60 publications
(16 citation statements)
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“…Stratal slicing has received much attention (e.g., Zeng, 2010Zeng, , 2013. De Groot et al (2010) indicate that, by greatly increasing the number of mapped horizons, one may achieve profound improvements in seismic interpretation. Some encouraging works include the phase-unwrapping approach (Stark, 2004), the dip-driven approach (de Groot et al, 2006), the predictive painting method (Fomel, 2010), and the global optimization algorithm (Hoyes and Cheret, 2011).…”
Section: Introductionmentioning
confidence: 99%
“…Stratal slicing has received much attention (e.g., Zeng, 2010Zeng, , 2013. De Groot et al (2010) indicate that, by greatly increasing the number of mapped horizons, one may achieve profound improvements in seismic interpretation. Some encouraging works include the phase-unwrapping approach (Stark, 2004), the dip-driven approach (de Groot et al, 2006), the predictive painting method (Fomel, 2010), and the global optimization algorithm (Hoyes and Cheret, 2011).…”
Section: Introductionmentioning
confidence: 99%
“…The model-driven mode is a way of slicing the seismic data relative to the framework horizons. The next stage in the workflow is to create a Wheeler diagram through 3D automated Wheeler Transformation [46]. Through Wheeler Transformation, the seismic data and attribute volumes were flattened in 3D along the auto-tracked horizons while at the same time, honoring the erosional events and non-depositional hiatuses [10].…”
Section: Methodsmentioning
confidence: 99%
“…To extract detailed stratigraphic information, more horizons are needed, which can only be done practically using a new generation of auto-tracking algorithms. One of these algorithms is the dip-steered auto-tracker of the HorizonCube method (de Groot et al 2010). The method starts by computing a dip/azimuth volume -called a SteeringCubefrom 2D/3D seismic data.…”
Section: Horizoncubementioning
confidence: 99%