The operations needed to decarbonise our energy systems increasingly involve faulted rocks in the subsurface. To manage the technical challenges presented by these rocks and the justifiable public concern over induced seismicity, we need to assess the risks. Widely used measures for fault stability, including slip and dilation tendency and fracture susceptibility, can be combined with Response Surface Methodology from engineering and Monte Carlo simulations to produce statistically viable ensembles for the analysis of probability. In this paper, we describe the implementation of this approach using custombuilt open source Python code (pfs – probability of fault slip). The technique is then illustrated using two synthetic datasets and two case studies drawn from active or potential sites for geothermal energy in the UK, and discussed in the light of induced seismicity focal mechanisms. The analysis of probability highlights key gaps in our knowledge of the stress field, fluid pressures and rock properties. Scope exists to develop, integrate and exploit citizen science projects to generate more and better data, and simultaneously include the public in the necessary discussions about hazard and risk.
DH first presented the core ideas in this paper at the Tectonic Studies Group AGM in Cardiff in 2014, and enjoyed discussions there with Dr Jonathan Turner (RWM Ltd). Thanks to former PhD student Dr Sarah Weihmann (now at BGR) and cosupervisor Dr Frauke Schaeffer (Wintershall DEA) for discussions about using oil industry wireline log data for quantifying geomechanical models. Thanks to Tom Blenkinsop (Cardiff) for the idea of using fault dips to estimate friction coefficients. GMT (Wessel et al., 2013) was used for the maps. SciPy (Virtanen et al., 2021), Numpy (Harris et al., 2020), and matplotlib 605 (Hunter, 2007) were used for the Python pfs code and Allmendinger et al. (2012) for various geomechanical and geometrical algorithms. We thank the reviewers for comments that improved the manuscript. DH acknowledges NERC funding from grant NE/T007826/1.
Data Availability StatementCode availability
The code used in this study is available at https://github.com/DaveHealy-github/pfs, last access: 6 January 2022 (https://zenodo.org/badge/latestdoi/377846715, Healy, 2021).
No data sets were used in this article.