Microseismic monitoring is a valuable technique to locate and characterize frac-
tures in unconventional reservoirs. The monitoring is usually carried out from a large
surface array of vertical-component receivers or a short downhole array of three-
component receivers. For a downhole array, P- and S-wave arrival-time picking is
typically required to process the microseismic data. Furthermore, arrival-time pick-
ing is done automatically considering the large volumes of microseismic data.
In this work, I propose a new methodology for automatic picking of P- and S-
wave arrivals of microseismic events recorded by downhole arrays. The methodology
consists of three steps: (1) For a single trace, intervals of possible arrivals are detected
using the conditional fuzzy c-means (CFCM) method. (2) These intervals are further
classi_ed into representing a P wave or an S wave using the information obtained
from polarization analysis. (3) The Akaike information criterion (AIC) picker is then
used on the P- and S-wave intervals to pick the corresponding arrival times. To
automatically validate the arrival picks, I test the Random-sampling-based Arrival
Time Event Clustering (RATEC) method.
The proposed methodology was tested on a real downhole microseismic data set
and was compared using fuzzy c-means (FCM) and with the short-term average over
long-term average (STA/LTA) method. To evaluate the automatic picking, manual
picks were used as a reference. For a time tolerance of ±5 ms, the percentage of correct
P- and S-wave arrival picks was 81% and 82% for the FCM methodology, and 77%
and 75% for the CFCM methodology. The STA/LTA was used to pick only P-wave
arrivals; it obtained 60% of correct picks. The RATEC method was used to vali-
date the arrival picks obtained by the FCM methodology. The percentage of correct
classi_cations was 93% and 87% for the P- and S-wave arrival picks respectively.
Based on the real data results, the best picking performance of the proposed
methodology is achieved using FCM. The FCM methodology is more robust to de-
tect and pick arrivals than the STA/LTA method. Additionally, the straightforward
implementation of the FCM method and AIC picker make the FCM methodology
implementation relatively simple.
| Date of Award | May 2019 |
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| Original language | English (US) |
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| Awarding Institution | - Physical Sciences and Engineering
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| Supervisor | Daniel Peter (Supervisor) |
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