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Using beamforming to maximise the detection capability of small, sparse seismometer arrays deployed to monitor oil field activities

Using beamforming to maximise the detection capability of small, sparse seismometer arrays deployed to monitor oil field activities
Using beamforming to maximise the detection capability of small, sparse seismometer arrays deployed to monitor oil field activities
Like most other industrial activities that affect the subsurface, hydraulic fracturing carries a 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 so we develop a beam-forming-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 beam-forming-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 so can be used to maximise the information generated when deployed to monitor industrial sites.
0016-8025
1582–1596
Verdon, James P.
a0bcb7a3-01cb-4375-8aad-7e687f0a872b
Kendall, J-Michael
0a1d10dd-0a0a-4b02-aefd-cde8741307ce
Hicks, Stephen P.
036d1b3b-bb7a-4a22-b2ce-71618a1723a3
Hill, Philip
b518dc7b-b6bb-4e6f-a699-e32efc547356
Verdon, James P.
a0bcb7a3-01cb-4375-8aad-7e687f0a872b
Kendall, J-Michael
0a1d10dd-0a0a-4b02-aefd-cde8741307ce
Hicks, Stephen P.
036d1b3b-bb7a-4a22-b2ce-71618a1723a3
Hill, Philip
b518dc7b-b6bb-4e6f-a699-e32efc547356

Verdon, James P., Kendall, J-Michael, Hicks, Stephen P. and Hill, Philip (2017) Using beamforming to maximise the detection capability of small, sparse seismometer arrays deployed to monitor oil field activities. Geophysical Prospecting, 65 (6), 1582–1596. (doi:10.1111/1365-2478.12498).

Record type: Article

Abstract

Like most other industrial activities that affect the subsurface, hydraulic fracturing carries a 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 so we develop a beam-forming-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 beam-forming-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 so can be used to maximise the information generated when deployed to monitor industrial sites.

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0.1111-1365.2478.12498.pdf - Accepted Manuscript
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More information

Accepted/In Press date: 18 December 2016
e-pub ahead of print date: 22 March 2017
Published date: 1 November 2017
Organisations: Ocean and Earth Science, Geology & Geophysics

Identifiers

Local EPrints ID: 405578
URI: http://eprints.soton.ac.uk/id/eprint/405578
ISSN: 0016-8025
PURE UUID: c4cbf18a-c3e6-45c0-a47e-863e4d28c528
ORCID for Stephen P. Hicks: ORCID iD orcid.org/0000-0002-7476-3284

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Date deposited: 06 Feb 2017 15:10
Last modified: 27 Apr 2022 04:16

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Contributors

Author: James P. Verdon
Author: J-Michael Kendall
Author: Stephen P. Hicks ORCID iD
Author: Philip Hill

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