Using beamforming to maximise the detection capability of small, sparse seismometer arrays deployed to monitor oil field activities
File(s)
Accepted version
Author(s)
Verdon, JP
Kendall, J-M
Hicks, SP
Hill, P
Type
Journal Article
Abstract
Like most other industrial activities that affect the subsurface, hydraulic fracturing carries the risk of reactivating pre‐existing faults and thereby causing induced seismicity. In some regions, regulators have responded to this risk by imposing traffic light scheme‐type regulations, where fracture stimulation programs must be amended or shut down if events larger than a given magnitude are induced. Some sites may be monitored with downhole arrays and/or dense near‐surface arrays, capable of detecting very small microseismic events. However, such monitoring arrangements will not be logistically or economically feasible at all sites. Instead, operators are using small, sparse arrays of surface seismometers to meet their monitoring obligations.
The challenge we address in this paper is to maximise the detection thresholds of such small, sparse, surface arrays so that they are capable of robustly identifying small‐magnitude events whose signal‐to‐noise ratios may be close to 1. To do this, we develop a beamforming‐and‐stacking approach, computing running short‐term/long‐term average functions for each component of each recorded trace (P, SH, and SV), time‐shifting these functions by the expected travel times for a given location, and performing a stack.
We assess the effectiveness of this approach with a case study using data from a small surface array that recorded a multi‐well, multi‐stage hydraulic fracture stimulation in Oklahoma over a period of 8 days. As a comparison, we initially used a conventional event‐detection algorithm to identify events, finding a total of 17 events. In contrast, the beamforming‐and‐stacking approach identified a total of 155 events during this period (including the 17 events detected by the conventional method). The events that were not detected by the conventional algorithm had low‐signal‐to‐noise ratios to the extent that, in some cases, they would be unlikely to be identified even by manual analysis of the seismograms. We conclude that this approach is capable of improving the detection thresholds of small, sparse arrays and thus can be used to maximise the information generated when deployed to monitor industrial sites.
The challenge we address in this paper is to maximise the detection thresholds of such small, sparse, surface arrays so that they are capable of robustly identifying small‐magnitude events whose signal‐to‐noise ratios may be close to 1. To do this, we develop a beamforming‐and‐stacking approach, computing running short‐term/long‐term average functions for each component of each recorded trace (P, SH, and SV), time‐shifting these functions by the expected travel times for a given location, and performing a stack.
We assess the effectiveness of this approach with a case study using data from a small surface array that recorded a multi‐well, multi‐stage hydraulic fracture stimulation in Oklahoma over a period of 8 days. As a comparison, we initially used a conventional event‐detection algorithm to identify events, finding a total of 17 events. In contrast, the beamforming‐and‐stacking approach identified a total of 155 events during this period (including the 17 events detected by the conventional method). The events that were not detected by the conventional algorithm had low‐signal‐to‐noise ratios to the extent that, in some cases, they would be unlikely to be identified even by manual analysis of the seismograms. We conclude that this approach is capable of improving the detection thresholds of small, sparse arrays and thus can be used to maximise the information generated when deployed to monitor industrial sites.
Date Issued
2017-11-01
Date Acceptance
2016-12-01
Citation
Geophysical Prospecting, 2017, 65 (6), pp.1582-1596
ISSN
0016-8025
Publisher
European Association of Geoscientists and Engineers
Start Page
1582
End Page
1596
Journal / Book Title
Geophysical Prospecting
Volume
65
Issue
6
Copyright Statement
© 2017 European Association of Geoscientists & Engineers. This is the accepted version of the following article, which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1111/1365-2478.12498
Identifier
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017389539&doi=10.1111%2f1365-2478.12498&partnerID=40&md5=0d3255c40a3c6555e792d77b33a641ab
Subjects
0404 Geophysics
Geochemistry & Geophysics
Notes
Cited By :9 Export Date: 5 February 2019
Publication Status
Published
Date Publish Online
2017-01-18