A novel muon detector for borehole density tomography

Alain Bonneville, Richard T. Kouzes, Jared Yamaoka, Charlotte Rowe, Elena Guardincerri, J. Matthew Durham, Christopher L. Morris, Daniel C. Poulson, Kenie Plaud-Ramos, Deborah J. Morley, Jeffrey D. Bacon, James Bynes, Julien Cercillieux, Chris Ketter, Khanh Le, Isar Mostafanezhad, Gary Varner, Joshua Flygare, Azaree T. Lintereur

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Muons can be used to image the density of materials through which they pass, including geological structures. Subsurface applications of the technology include tracking fluid migration during injection or production, with increasing concern regarding such timely issues as induced seismicity or chemical leakage into aquifers. Current density monitoring options include gravimetric data collection and active or passive seismic surveys. One alternative, or complement, to these methods is the development of a muon detector that is sufficiently compact and robust for deployment in a borehole. Such a muon detector can enable imaging of density structure to monitor small changes in density – a proxy for fluid migration – at depths up to 1500 m. Such a detector has been developed, and Monte Carlo modeling methods applied to simulate the anticipated detector response. Testing and measurements using a prototype detector in the laboratory and shallow underground laboratory demonstrated robust response. A satisfactory comparison with a large drift tube-based muon detector is also presented.

Original languageEnglish (US)
Pages (from-to)108-117
Number of pages10
JournalNuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Volume851
DOIs
StatePublished - Apr 11 2017

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Instrumentation

Fingerprint

Dive into the research topics of 'A novel muon detector for borehole density tomography'. Together they form a unique fingerprint.

Cite this