Fast first arrival picking algorithm for noisy microseismic data
Dowan Kim 1 Joongmoo Byun 1 3 Minho Lee 1 Jihoon Choi 1 Myungsun Kim 1 21 Department of Natural Resources and Geoenvironmental Engineering, Hanyang University, Haengdang 1-dong, Seongdong-gu, Seoul 133-791, Korea.
2 Present address: Korea Institute of Geoscience and Mineral Resources, Center for Deep Subsurface Research, Daejeon 34132, Korea.
3 Corresponding author. Email: jbyun@hanyang.ac.kr
Exploration Geophysics 48(2) 131-136 https://doi.org/10.1071/EG15120
Submitted: 26 November 2015 Accepted: 26 November 2015 Published: 20 January 2016
Abstract
Most microseismic events occur during hydraulic fracturing. Thus microseismic monitoring, by recording seismic waves from microseismic events, is one of the best methods for locating the positions of hydraulic fractures. However, since microseismic events have very low energy, the data often have a low signal-to-noise ratio (S/N ratio) and it is not easy to pick the first arrival time. In this study, we suggest a new fast picking method optimised for noisy data using cross-correlation and stacking. In this method, a reference trace is selected and the time differences between the first arrivals of the reference trace and those of the other traces are computed by cross-correlation. Then, all traces are aligned with the reference trace by time shifting, and the aligned traces are summed together to produce a stacked reference trace that has a considerably improved S/N ratio. After the first arrival time of the stacked reference trace is picked, the first arrival time of each trace is calculated automatically using the time differences obtained in the cross-correlation process. In experiments with noisy synthetic data and field data, this method produces more reliable results than the traditional method, which picks the first arrival time of each noisy trace separately. In addition, the computation time is dramatically reduced.
Key words: cross-correlation, first arrival picking, hydraulic fracturing, microseismic monitoring, stacking.
References
Akram, J., 2011, Automatic P-wave arrival time picking method for seismic and microseismic data: 2011 CSPG (Canadian Society of Petroleum Geologists) CSEG (Canadian Society of Exploration Geophysicists) CWLS (Canadian Well Logging Society) convention abstracts.Baer, M., and Kradolfer, U., 1987, An automatic phase picker for local and teleseismic events: Bulletin of the Seismological Society of America, 77, 1437–1445
Earle, P. S., and Shearer, P. M., 1994, Characterization of global seismograms using an automatic-picking algorithm: Bulletin of the Seismological Society of America, 84, 366–376
Gilliland, E. S., Ripepi, N., Conrad, M., Miler, M. J., and Karmis, M., 2013, Selection of monitoring techniques for a carbon storage and enhanced coalbed methane recovery pilot test in the Central Appalachian Basin: International Journal of Coal Geology, 118, 105–112
| Selection of monitoring techniques for a carbon storage and enhanced coalbed methane recovery pilot test in the Central Appalachian Basin:Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXht1SrtLnK&md5=7c12d41a45fad97b7ac6b007f79a1eabCAS |
Häring, M. O., Schanz, U., Ladner, F., and Dyer, B. C., 2008, Characterization of the Basel 1 enhanced geothermal system: Geothermics, 37, 469–495
| Characterization of the Basel 1 enhanced geothermal system:Crossref | GoogleScholarGoogle Scholar |
Lee, M., Kim, D., Choi, J., Kim, M., and Byun, J., 2013, Improved MER method for accurate first arrival picking: Korean Society for Geosystem Engineering 101st conference, Expanded Abstracts, 95–97.
Maxwell, S., 2009, Microseismic location uncertainty: Canadian Society of Exploration Geophysicists - Recorder, 34, 41–46
Morita, Y., and Hamaguchi, H., 1984, Automatic detection of onset time of seismic waves and its confidence interval using the autoregressive model fitting: Zisin (Journal of the Seismological Society of Japan. 2nd ser.), 37, 281–293
Munro, K. A., 2004, Automatic event detection and picking of P-wave arrivals: CREWS Research Report, University of Calgary.
Sleeman, R., and van Eck, T., 1999, Robust automatic p-phase picking: an on-line implementation in the analysis of broadband seismogram recordings: Physics of the Earth and Planetary Interiors, 113, 265–275
| Robust automatic p-phase picking: an on-line implementation in the analysis of broadband seismogram recordings:Crossref | GoogleScholarGoogle Scholar |
Song, Y., Park, M., Jeon, J., Lee, M., Cho, C., and Lee, T., 2012, Construction of a microseismicity observation network with borehole accelerometers for monitoring geothermal reservoir: Journal of the Korean Society for Geosystem Engineering, 49, 487–497
Tezuka, K., and Niitsuma, H., 2000, Stress estimated using microseismic clusters and its relationship to fracture systems of the Hijiori hot dry rock reservoir: Engineering Geology, 56, 47–62
| Stress estimated using microseismic clusters and its relationship to fracture systems of the Hijiori hot dry rock reservoir:Crossref | GoogleScholarGoogle Scholar |
Wong, J., Han, L., Bancroft, J. C., and Stewart, R. R., 2009, Automatic time-picking of first arrivals on noisy microseismic data: Technical Report, CREWES Research Report, University of Calgary.