Published: 02/02/2022
Published: 02/02/2022
An operator wanted to measure formation slowness in a horizontal well and use wellbore images with surface seismic interpretation to determine the structural features. The operator did not have detailed structural information, and the many subseismic lithological boundaries would be used to drill subsequent development wells.
Both standard and slim dipole sonic logging tools were deployed to obtain images along the lateral. To compare the sonic images, the migration results were fitted along the well to generate a final 2D image of the subsurface. The P-wave migrated images generated from monopole data showed ~200-ft coverage away from the wellbore using both the standard and slim tools, and the slim dipole sonic data mapped the reflection boundaries observed on standard sonic data. The S-wave migration images from dipole data showed coverage of ~200 ft using the standard sonic logging tool, whereas the slim dipole sonic data showed ~150 ft of data coverage. This was due to the shorter recording time of slim dipole sonic measurement. Recording time is now flexible through programable firmware with a user interface that enables multiple options.
The major boundaries were easily mapped with both standard and slim sonic measurements. In addition to the boundary mapping, information on the high-angle reflectors was extracted from the recorded data using an automated 3D far-field sonic service. The service provides quantitative dip and azimuth of reflectors, and the mapping provided can be directly used in geological modeling and formation evaluation.
The reflectors were below seismic resolution but confirmed by nearby offset vertical wells intersecting the horizons. The 3D far-field sonic service has shown the potential to fill the image resolution gap between seismic methods and traditional near-field logging.
Using a 3D far-field sonic service, the operator of a field obtains distance, true dip, and azimuth of high-angle reflectors beyond the reach of standard sonic logging—greatly improving geological modeling and formation evaluation to reduce risk and uncertainty.
