Abstract
Historically, Silurian pinnacle reef complexes in the Michigan Basin have been largely identified using 2D seismic with very little research on the reservoir characterization of these reefs using 3D seismic data. By incorporating a high-resolution 3D dataset constrained by a well-studied and data-rich paleoreef reservoir, the Puttygut reef, seismic attributes were correlated to petrophysical properties through machine learning and self-organizing maps (SOMs). A suite of structural and frequency-based attributes was calculated from pre-stack time migrated (PSTM) seismic data, with only a subset of them selected as SOM inputs. Structural attributes enhanced details in the reef but frequency attributes were overall more useful for correlating with reservoir quality. A strong relationship between certain combination percentages of attributes and certain sections of the reef with porosity and permeability was found after the SOM results were compared to wireline log and core analysis data. Areas with high permeability and porosity correlated with the average frequency and spectral decomposition at 29 and 81 Hz. Areas with high porosity and varying permeability correlated with the average frequency and spectral decomposition at 29, 57, and 81 Hz. Areas with intermediate porosity correlated with the average frequency and spectral decomposition at 29 and 57 Hz. The efficacy of the procedure was then demonstrated on two nearby reefs with very similar results.
Highlights
The focus of this study was to characterize features in ancient reef reservoirs that are at and below conventional vertical seismic resolution by creating a machine learning and multi-attribute analysis workflow
The following section details observations of the single attributes results extracted onto horizons picked by an expert geologist that works in the Michigan Basin
Four type wells that had the most complete suite of porosity and permeability data were chosen for this task: P-106, P-201, P-102, and P-103
Summary
The focus of this study was to characterize features in ancient reef reservoirs that are at and below conventional vertical seismic resolution by creating a machine learning and multi-attribute analysis workflow. In addition to the production related motivation to better characterize these reef reservoirs, they have been used for gas storage and CO2 sequestration over the past 50 years [8]. The majority of 3D seismic acquired and processed over these reefs in the past 20 years have been for gas storage and CO2 sequestration projects, with some of the gas storage reefs having core-rich datasets which are utilized in this study for correlation to seismic attributes. These are the first data sets of their kind where both 3D seismic and multiple cores are available within the same reef complex
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