Machine learning investigation of injection-seismicity in Rotokawa geothermal field

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dc.contributor.authorYu, Pengliang
dc.contributor.authorDempsey, David
dc.contributor.authorCalibugan A
dc.contributor.authorArcher R
dc.date.accessioned2023-09-13T01:59:49Z
dc.date.available2023-09-13T01:59:49Z
dc.date.issued2021
dc.date.updated2023-06-08T21:09:16Z
dc.description.abstractUnderstanding the injection-seismicity relationship in geothermal reservoirs can provide insight into reservoir connectedness. One challenge is that, in real fields, fault and reservoir complexity make it difficult to apply simple analytical models to understand the data. Here, we use a machine learning technique called time-series feature engineering to study relationships between aspects of fluid injection and microearthquakes in Rotokawa geothermal field, New Zealand. We took four years of injection data between 2012 and 2016 and sliced it into smaller sub-windows. For each window, the average seismicity in a look-back period was computed, and then binary label of 1 was assigned if it exceeded a threshold. Automatic time series feature extraction from the raw and transformed injection data in each window was performed using Python package tsfresh. Significant features of the data were identified on the basis of distribution discrepancy between the two labels. The results show that the injection rate at some wells is a predictor of long term (fortnightly) earthquake rates. At other wells, there is a poor correlation between injection rate and seismicity. We have been unable to find any link between rapid changes in injection rate and seismicity spikes, as suggested by some theoretical models.
dc.identifier.citationYu P, Dempsey D, Calibugan A, Archer R (2021). Machine learning investigation of injection-seismicity in Rotokawa geothermal field. online: 43rd NZ Geothermal Workshop.
dc.identifier.urihttps://hdl.handle.net/10092/106164
dc.rightsAll rights reserved unless otherwise stated
dc.rights.urihttp://hdl.handle.net/10092/17651
dc.subjectMicroseismicity
dc.subjectinjection
dc.subjecttime series feature engineering
dc.subjectseismicity rate
dc.subjectsignificant features
dc.subjectp-value
dc.subject.anzsrc37 - Earth sciences::3706 - Geophysics
dc.subject.anzsrc40 - Engineering::4005 - Civil engineering::400506 - Earthquake engineering
dc.subject.anzsrc46 - Information and computing sciences::4611 - Machine learning
dc.subject.anzsrc37 - Earth sciences::3706 - Geophysics
dc.subject.anzsrc40 - Engineering::4005 - Civil engineering::400506 - Earthquake engineering
dc.titleMachine learning investigation of injection-seismicity in Rotokawa geothermal field
dc.typeConference Contributions - Published
uc.collegeFaculty of Engineering
uc.departmentCivil and Natural Resources Engineering
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