Details of Award
NERC Reference : NE/L009226/1
Useful noise: study of microseismic noise characteristics and implementation within synthetic full-waveform microseismic dataset for benchmarking
Training Grant Award
- Lead Supervisor:
- Dr DA Angus, University of Leeds, School of Earth and Environment
- Grant held at:
- University of Leeds, School of Earth and Environment
- Science Area:
- Earth
- Overall Classification:
- Earth
- ENRIs:
- Environmental Risks and Hazards
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- None
- Abstract:
- Microseismic (or passive seismic) monitoring is commonly used in many geo-industrial applications (e.g. investigating geological storage risks for CO2 and nuclear waste disposal, monitoring hydraulic stimulation for geothermal and shale-gas exploitation, and geomechanical deformation in hydrocarbon reservoir production). The recent large growth in microseismic monitoring for geo-industrial applications is being led, in large part, by advances in microseismic instrumentation and processing algorithms. However, many of the processing advances have not been quantitatively benchmarked. For instance, location error analyses of microseismic events (i.e. the most fundamental measurement in microseismic monitoring) ignore the influence of the velocity model. More importantly, error in event location and velocity model uncertainty will feed into other microseismic attribute errors and uncertainty (e.g., source failure mechanism). For geoenergy and geo-engineering applications, it is crucial that microseismic activity be accurately monitored with a strong degree of quantitative certainty in the measurement error for public acceptance, government regulation, risk assessment and aversion and economic management. The proposed PhD studentship will develop a synthetic microseismic dataset similar to the highly successful Marmousi dataset used to benchmark seismic imaging algorithms. The Marmousi dataset represented the first time the industry benchmarked many imaging algorithms to provide a quantitative measure of their accuracy and comparison between algorithms. The usefulness of the Marmousi experiment can be seen in the recent major industry investment into a more complex synthetic test models (e.g. SEG-SEAM Phase 1 synthetic models at 1,300,000 USD). Specifically, the project will develop a 3D microseismic waveform dataset that can be used to test microseismic processing and interpretation algorithms. The project will consist of three work packages: WP1 - study of noise characteristics of surface and borehole arrays in hydraulic stimulation programmes, WP2 - development of 3D isotropic and anisotropic elastic models and full waveform microseismic dataset for unconventional hydrocarbon systems, WP3 - implementation of noise characterisation to synthetic waveform data for synthetic case study. A comprehensive characterisation of microseismic noise will enable UK industry to develop advanced processing algorithms to either remove or utilise microseismic noise for enhanced imaging of fracturing. Furthermore, the development of an advanced full-waveform microseismic waveform dataset will provide the UK microseismic sector the capability to benchmark and calibrate processing and interpretation algorithms as well as potential future funding for a more comprehensive type industry standard test dataset.
- NERC Reference:
- NE/L009226/1
- Grant Stage:
- Completed
- Scheme:
- DTG - directed
- Grant Status:
- Closed
- Programme:
- Industrial CASE
This training grant award has a total value of £83,515
FDAB - Financial Details (Award breakdown by headings)
| Total - Fees | Total - Student Stipend | Total - RTSG |
|---|---|---|
| £16,226 | £56,292 | £11,000 |
If you need further help, please read the user guide.