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Stress prediction, fracture detection, and multiple attribute analysis of seismic attributes in a Barnett Shale gas reservoir, Fort Worth Basin, Texas
Keenan, James G.
Keenan, James G.
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2015
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2016-02-01
Abstract
The RCP Barnett Shale Project, sponsored by EOG Resources Inc., returns to the North Texas shale gas play that began the North American unconventional energy revolution. The project focuses on the paramount factor affecting the productivity of horizontal wells in the Barnett Shale: the reservoir's ability to conduct hydrocarbons when hydraulically stimulated. The study area is a natural gas producing area of the Barnett Shale in the Fort Worth Basin of North Texas. To facilitate this project, EOG provided RCP with electric well logs, 3D P-wave seismic, microseismic, and engineering data from the area of the Barnett Merge 1 seismic survey under the terms of a confidentiality agreement. In compliance with that agreement, certain information, such as the names and locations of wells, seismic survey coordinates, specific engineering practices, and production volumes will not be disclosed. The objective of this investigation was to evaluate the utility of seismic attributes in detecting geologic heterogeneities likely to influence the occurrence of sweet spots in the Barnett Shale gas reservoir within the study area. Emphasis was placed on the detection of changing stress conditions and natural fractures. Azimuthal interval velocity analysis was found to be an effective tool for the detection of changing stress fields. The analysis of azimuthal amplitude data suggests that it can be used to map changes in azimuthal anisotropy with higher spatial resolution than azimuthal velocities. Based on the examination of incoherence, curvature, and azimuthal velocity attributes, the structural control on local stress variations and fracture trends is interpreted to be the occurrence of polygonal fault systems influenced by transpressional tectonic forces. The same combination of attributes can be leveraged to improve the indirect detection of natural fractures. Multiple attribute statistical analysis confirmed relationships between several attributes and productivity and highlighted the importance of accounting for production variability due to changing completion practices, but the construction of a multivariate statistical model assessing productivity in the study area was deemed intractable. Attributes can be examined concurrently to identify areas where conditions are favorable for the creation of complex fracture networks and to identify portions of existing wellbores where refracturing may contact reservoir bypassed during the original hydraulic stimulation.
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