‘Useful noise: microseismic noise characteristics and implementation within a synthetic reservoir full-waveform microseismic dataset for benchmarking’
This fully-funded NERC studentship is available to UK/EU candidates. If you are an EU candidate you should check whether you are eligible for a full award. The funding will pay tuition fees, a tax-free stipend, and research training and support grant for up to 3.5 years.
Supervisors: Dr Doug Angus and Dr Sebastian Rost (CASE award with Pinnacle (Rock Talk) in Cornwall)
Microseismic monitoring is commonly used in many geo-industrial applications (geological storage risks for CO2/nuclear waste disposal, monitoring hydraulic stimulation for geothermal/shale-gas exploitation, and geomechanical deformation in hydrocarbon production). Growth in microseismic monitoring is being driven by advancements in microseismic instrumentation and processing algorithms, yet many of the processing advances have not been quantitatively benchmarked. For instance, location error analyses of microseismic events (the most fundamental microseismic measurement) ignore the influence of the velocity model and often assume the interfering noise has certain properties (e.g. random). These errors in event location and velocity model uncertainty feed into other microseismic attribute errors and uncertainty (e.g. failure mechanism).
The studentship will develop a synthetic microseismic dataset similar to the Marmousi dataset used to benchmark seismic imaging algorithms and will consist of three work packages: with specific objectives:
- WP1. Noise characterisation: Seismic noise consists of instrument noise, ambient noise and seismically generated noise. The main research thrust will be in characterising all three types of noise in microseismic hydraulic stimulation monitoring settings. The research will make use of the Pinnacleâs experience with microseismic instrumentation, as well as operational and in-field characteristics of instrument and the ambient noise field. Separation of ambient and seismically generated noise will make use of array directivity techniques and conventional techniques (e.g. as used in array triggering algorithms). Further noise characteristics will be quantified using stochastic interferometry. An added benefit of noise characterisation is the possibility of making step changes in microseismic processing for noise elimination (e.g. improved sampling criteria for noise suppression in surface seismics). Finally, elastic 3D wave propagation methods will be used to simulate noise fields and their associated characteristics.
- WP2. Waveform modeling: A suite of elastic 3D models representative of unconventional reservoir systems will be generated, exploring the influence of heterogeneity and anisotropy on the seismic wavefield. Synthetic microseismic wavefields will be generated using academic 3D finite-difference codes developed for high-performance computers. One or more case studies will be used as a template for velocity model, geometry and frequency of field microseismic datasets, and site specific noise characteristics.
- WP3. Case study: Noise characterisation results from WP1 will be implemented on synthetic waveform data from WP2. The synthetic data will be processed similar to the real case study dataset(s), and the results will be compared to provide a quantitative measure of the complexity of the synthetic data.Â
For information about how to apply, visit:
http://www.see.leeds.ac.uk/admissions-and-study/research-degrees/igt/useful-noise/
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