Carbonate reservoirs provide numerous complications for accurate reservoir characterization and management. A seismic survey acquired in the Fall of 2019 over a Silurian (Niagaran) pinnacle reef gas storage reservoir to investigate the applicability of unsupervised machine learning techniques on attributes generated on angle stacks to enhance facies identification in seismic data. Internal heterogeneities are identified within the reef core reservoir facies on the near angle self-organizing map not seen in a SOM calculated on the PSTM volume. Underlying low porosity bioherm facies are accurately revealed on the far angle stack SOM. The spatial distribution of facies is mapped, and will be utilized to constrain future geostatistical models.

Presentation Date: Tuesday, October 13, 2020

Session Start Time: 1:50 PM

Presentation Time: 2:40 PM

Location: 360D

Presentation Type: Oral

Keywords:
upstream oil & gas, facies, reservoir characterization, ray reef, artificial intelligence, heterogeneity, reef core, seg international exposition, angle stack som, michigan basin
Subjects:
Reservoir Characterization, Information Management and Systems, Seismic processing and interpretation, Artificial intelligence
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2020. Society of Exploration Geophysicists
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