Batch automated image processing of 2D seismic data for salt discrimination and basin-wide mapping

Abstract

We have explored the technical utility of analyzing massive sets of digital 2D seismic data, collected and processed in dozens of different surveys, conducted more than 25 years ago, using batch, automated and unsupervised pattern recognition techniques to produce a basin-wide map of the top of salt. This workflow was developed for the United States portion of the Gulf of Mexico to detect top-salt boundaries on 2D poststack migrated lines. Texture-based attributes as well as novel, reflector-based attributes were used to discriminate between salt and nonsalt on each seismic line. Explicit measures of accuracy were not calculated because the data are unlabeled, but an assessment of confidence was used to score the boundaries. The depth to the top of the salt was estimated for more than 67% of the study area ([Formula: see text] or [Formula: see text]), 17% of the study area had insufficient data for processing and analysis, and 16% of the area did not meet confidence requirements for inclusion. The final results compared well with published maps of salt and the locations of salt-trapped fields. Reliable mapping of salt deeper than 6 s two-way time could not be achieved with this data set and approach because many seismic images had indistinguishable features at this depth. The computing time was greater than linear in the number of lines, but parallelization and changes in hardware configuration could reduce the run time of about three weeks to about three days.