Unless the capacity is exceeded, we will not respond to your email. Attendance is free, just bring along your lunch.
RSVP NOW for the next Microseismic User Group (MUG) event.
If you have any questions, please contact:
Paige Mamer, Paige.Mamer@tgs.com,
Johnny Wentzel, Johnny.Wentzel@esgsolutions.com
Abstract
Microseismic hydraulic fracture imaging is controlled by the accuracy of individual microseismic event locations, related to the monitoring array configuration, data quality (e.g. signal-to-noise ratio) and the velocity model. In certain configurations, however, complex microseismic signals can be recorded when significant amplitudes of refracted or reflected waves interfering with direct arrivals. Such signal complexity can lead to substantial inaccuracies in microseismic locations if an inconsistent seismic phase is matched during the microseismic processing. Reprocessing of microseismic data can then result in dramatic location differences if the signal phases are interpreted differently, even in a scenario where similar velocity models are used.
In this case study, a downhole microseismic array was used to monitor a Woodford Shale hydraulic fracture treatment. Initial processing resulted in located events significantly above the horizontal treatment well and laterally offset from the initiation point, along with a lack of microseismicity at the initiation points. Interpretation of the microseismic image was compromised and undermined the value of information to confidently interpret the fracture geometry. A quality control evaluation was performed to assess the location patterns, utilizing synthetic microseismic signals computed from different origin points. The stages in question were found to suffer from significant signal complexity. The microseismicity was relocated using the synthetic signals to guide phase interpretation, which resulted in locating microseismicity much closer to the fracture initiation points. Discrepancies between the original and reprocessed results were assessed by correlating synthetic waveforms from the corresponding locations with the recorded signals. The reprocessed results were shown to better correlate with the recorded waveforms than the original, disperse locations.
The case study represents an example of critically evaluating differences between various processed results, to quantify the match with the recorded signals. The workflow allows for quantification of the confidence in the microseismic results through correlation of the match with the recorded data, to improve microseismic confidence.
