The size of the individual seismic surveys has increased over the last decade, along with the generation of megamerge and even larger, what some operators call “gigamerge” surveys. The number of useful attribute volumes has also increased, such that interpreters may need to integrate terabytes of data. During the past several years, various machine learning methods including unsupervised, supervised and deep learning have been developed to better cope with such large amounts of information. In this study we apply several unsupervised machine learning methods to a seismic data volume from the Barents Sea, on which we had previously interpreted shallow high-amplitude anomalies using traditional interactive interpretation workflows. Specifically, we apply k-means, principal component analysis, self-organizing mapping and generative topographic mapping to a suite of attributes and compare them to previously generated P-impedance, porosity and Vclay displays, and find that self-organized mapping and the generative topographic mapping provide additional information of interpretation interest.
In the late 1980s, seismic facies analysis was carried out on 2D seismic data by visually examining the seismic waveforms that can be characterized by their amplitude, frequency and phase expression. Such information would be posted on maps and contoured to generate facies maps. As seismic data volumes increased in size with the adoption of 3D seismic data in the early 1990s, interpreters found that 3D seismic attributes highlighted patterns that facilitated the human recognition of geologic features on time and horizon slices, thereby both accelerating and further quantifying the interpretation. More recently, computer-assisted seismic facies classification techniques have evolved. Such methods or workflows examine seismic data or their derived geometric, spectral, or geomechanical attributes and assign each voxel to one of a finite number of classes, each of which is assumed to represent seismic facies. Such seismic facies may or may not represent geologic facies or petrophysical rock types. In this workflow, well log data, completion data, or production data are then used to determine if a given seismic facies is unique and should be lumped (or “clustered”) with other similar facies determined from attributes with similar attribute expression.
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