Microscale Imaging for Unconventional Plays Track Technical Presentation:
Unlocking Whole Core CT Data for Advanced Description and Analysis
American Association of Petroleum Geophysicists (AAPG)
2016 Annual Convention and Exposition Technical Presentation
Tuesday June 21st at 4:15 PM, Hall B, Room 2, BMO Centre, Calgary
Presented by: Brendon Hall, Geoscience Applications Engineer, Enthought, and Andrew Govert, Geologist, Cimarex Energy
It has become an industry standard for whole-core X-ray computed tomography (CT) scans to be collected over cored intervals. The resulting data is typically presented as static 2D images, video scans, and as 1D density curves.
However, the CT volume is a rich data set of compositional and textural information that can be incorporated into core description and analysis workflows. In order to access this information the raw CT data initially has to be processed to remove artifacts such as the aluminum tubing, wax casing and mud filtrate. CT scanning effects such as beam hardening are also accounted for. The resulting data is combined into contiguous volume of CT intensity values which can be directly calibrated to plug bulk density.
With this processed CT data:
- The volume can be analyzed to identify bedding structure, dip angle, and fractures.
- Bioturbation structures can often be easily identified by contrasts in CT intensity values due to sediment reworking or mineralization.
- CT facies can be determined by segmenting the intensity histogram distribution. This provides continuous facies curves along the core, indicating relative amounts of each material. These curves can be integrated to provide estimates of net to gross even in finely interbedded regions. Individual curves often exhibit cyclic patterns that can help put the core in the proper sequence stratigraphic framework.
- The CT volume can be analyzed to classify the spatial relationships between the intensity values to give a measure of texture. This can be used to further discriminate between facies that have similar composition but different internal organization.
- Finally these CT derived features can be used alongside log data and core photographs to train machine learning algorithms to assist with upscaling the core description to the entire well.