Hydro-Mechanics of Fluid-Induced Seismicity in the Context of the Green-Energy Transition

Lead Research Organisation: University of Bristol
Department Name: Earth Sciences


Green-energy transition technologies such as carbon storage, geothermal energy extraction, hydrogen storage, and compressed-air energy storage, all rely to some extent on subsurface injection or extraction of fluids. This process of injection and retrieval is well known to industry, as it has been performed all over the world, for decades.

Fluid injection processes create mechanical disturbances in the subsurface, leading to local or regional displacements that may result in tremors. In the vast majority of cases, these tremors are imperceptible to humans, and have no effect on engineered structures. Nonetheless, in recent years, low magnitude induced seismic events have had profound consequences on the social acceptance of subsurface technologies, including the halting of natural gas production at the Groningen field in the Netherlands, halting of carbon storage experiments in Spain, halting of geothermal energy projects in Switzerland, and the moratorium on UK onshore gas extraction. In light of the seismic events of increasing severity recently measured during geothermal mining in Cornwall, the need to develop a rigorous fundamental understanding of induced seismicity is clear, significant, and timely, in order to prevent induced seismicity from jeopardising the ability to effectively develop the green energy transition.

Most mathematical models that are used to predict and understand tremors rely on past observations of natural tremors and earthquakes. However, fluid-driven displacement in the subsurface is a controlled event, in which the properties of the injected fluids and the conditions of injection can be adjusted and optimised to avoid large events from happening. This project aims to develop a fundamental understanding of how the conditions of subsurface rocks, and the way in which fluid is injected in these rocks, affect the amount of seismicity that may be produced.

We will analyse in detail the behaviour of fluid-driven seismic events, and will develop a physically realistic model based on computer simulations, novel laboratory experiments, and comprehensive field observations. Our model will characterise the relationships between specific subsurface properties, the nature of the fluid injection, and the severity of the seismic event. These findings will be linked to hazard analysis, to identify the conditions under which processes such as carbon storage, deep geothermal energy extraction, and compressed-air energy storage, are more or less likely to create tremors. We will also investigate how to best share our scientific findings with regulators and the general public, so as to maximise the impact of this work.

This project will lead to an improved understanding of the processes and conditions that underpin the severity of induced seismic events, with a vision of developing strategies that will improve our ability to prevent and control these events. This project will also provide the scientific basis to improve precision and cost-effectiveness of scientific instruments that are used to monitor the subsurface, so that we can identify tremors as they occur, and better interpret what is causing them as we observe them.

In the short term, we need to develop these models so that regulators and decision-makers can develop policies based on scientific evidence, using a variety of analysis tools that inter-validate each other, thereby ensuring that their predictions are robust. This is an important step in supporting the ability of developing a resilient, diversified, sustainable, and environmentally responsible energy security strategy for the UK.

In the long term, by creating confidence in the understanding of these subsurface events, and demonstrating evidence of our ability to control them, we will lead the UK into an era where humans understand why certain seismic events have occurred, allowing them to potentially develop mechanisms to forecast their occurrence, and reduce their severity.


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