Laboratory-scale microseismic data analysis

Microseismic monitoring involves recording ground movement at seismic stations (surface or downhole) to determine the locations of microseismic events and their mechanisms at the source. For geological carbon storage, petroleum production, enhanced geothermal energy, mining, etc., this method may be used to detect faulting and fracturing resulting not only from pressure changes and fluid flow in the reservoir, but also stress field change/rotation in the overburden. In the laboratory, the same microseismic monitoring technique (called acoustic emission monitoring, AE) may be used to detect and characterize microcracking and failure of rock samples as they deform, using acoustic sensors in direct contact with the rock (recently, using fibre optic sensor as well).

Recently, we have acquired a large amount of AE datasets through a recent CLIMIT project. Tested cores include both analogue and reservoir and cap rocks from the North Sea. The available datasets are not only extensive but also quite unique in their focus on CO2 storage potential in the North Sea (e.g. Longship and Aurora). NGI is still working with these datasets and we would like to recruit one or two MSc students who can work on them together with us.

Laboratory AE data comprises of thousands of seismic waveforms measured in voltage across multiple acoustic sensors (typically 10-20). Processing of these high-volume data involves picking wave arrival times and their amplitudes—to locate events and determine their source mechanisms—and is often done manually for best results. This approach may time consuming and subject to user bias.

Currently, we plan for two internship topics: (1) using statistical methods, quantify the influence of user- bias and data-noise on the interpretation of results; and (2) automate the data processing methods using intelligent (i.e. machine learning) techniques to best reproduce the manual analysis. We will work on both of triggered-based and continuous recording-based. The latter is related to aseismic or slow events, while the former is related to more pronounced seismic events. Both of the two types of even are relevant for CO2 storage monitoring.

The dataset for this study was produced during laboratory tests on North Sea materials relevant to CO2 storage. Testing was performed at NGI and CSIRO, Perth, Australia during NGI-led project IGCCS (NFR CLIMIT project: Induced-seismicity geomechanics for controlled CO2 storage in the North Sea). Dr Joel Sarout at CSIRO will also be advised together with Dr Luke Griffiths and Dr Joonsang Park at NGI. In addition to the two topics mentioned above, we may consider and define new topics in line with the candidate’s interests.

More details of the study and time frame can be discussed with UiO and the student(s). The period of work is early 2021 to mid-2022 or can be discussed and adjusted. This study requires some understanding of seismic wave propagation and good programming skills with, for example, MATLAB, Python.

Published Jan. 18, 2021 4:02 PM - Last modified Jan. 18, 2021 4:02 PM