Abstract: Techniques to match a signature in seismic data with a seismic attribute space. A method includes automatically selecting a first plurality of seismic attributes corresponding to seismic data as first selected seismic attributes, combining the first selected seismic attributes into a first realization of attributes, performing a first cluster analysis on the first realization of attributes to generate a first clustered volume, selecting a region of interest (ROI) in the seismic data, projecting the ROI onto the first clustered volume to generate a first signature, determining a first level of correlation between the ROI and the first signature, and determining whether the first level of correlation between the ROI and the first signature exceeds a predetermined threshold and outputting a first correlation volume corresponding to the first signature when the first level of correlation between the ROI and the first signature exceeds the predetermined threshold.

Abstract: Techniques to match a signature in seismic data with a seismic attribute space. A method includes automatically selecting a first plurality of seismic attributes corresponding to seismic data as first selected seismic attributes, combining the first selected seismic attributes into a first realization of attributes, performing a first cluster analysis on the first realization of attributes to generate a first clustered volume, selecting a region of interest (ROI) in the seismic data, projecting the ROI onto the first clustered volume to generate a first signature, determining a first level of correlation between the ROI and the first signature, and determining whether the first level of correlation between the ROI and the first signature exceeds a predetermined threshold and outputting a first correlation volume corresponding to the first signature when the first level of correlation between the ROI and the first signature exceeds the predetermined threshold.

Abstract: Systems and methods are disclosed that include generating reservoir property profiles corresponding to reservoir properties for pseudo wells based on reservoir data, generating seismic attributes for the pseudo wells, and training a machine learning model by comparing the reservoir property profiles against the seismic attributes. In this manner, the machine learning model may be used to predict reservoir properties for use with seismic exploration above a region of a subsurface that contains structural or stratigraphic features conducive to a presence, migration, or accumulation of hydrocarbons.

Abstract: Systems and methods are disclosed that include generating reservoir property profiles corresponding to reservoir properties for pseudo wells based on reservoir data, generating seismic attributes for the pseudo wells, and training a machine learning model by comparing the reservoir property profiles against the seismic attributes. In this manner, the machine learning model may be used to predict reservoir properties for use with seismic exploration above a region of a subsurface that contains structural or stratigraphic features conducive to a presence, migration, or accumulation of hydrocarbons.