Arrival-time picking methodology using fuzzy c-means and Akaike information criterion for downhole microseismic data
Type
ThesisAuthors
Valero Cano, Eduardo
Advisors
Peter, Daniel
Committee members
Finkbeiner, ThomasEisner, Leo
Schuster, Gerard T.

Program
Earth Science and EngineeringKAUST Department
Physical Science and Engineering (PSE) DivisionDate
2019-05Permanent link to this record
http://hdl.handle.net/10754/655681
Metadata
Show full item recordAbstract
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.Citation
Valero Cano, E. (2019). Arrival-time picking methodology using fuzzy c-means and Akaike information criterion for downhole microseismic data. KAUST Research Repository. https://doi.org/10.25781/KAUST-AYKT4ae974a485f413a2113503eed53cd6c53
10.25781/KAUST-AYKT4