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James P. Verdon

Researcher at University of Bristol

Publications -  116
Citations -  2322

James P. Verdon is an academic researcher from University of Bristol. The author has contributed to research in topics: Induced seismicity & Microseism. The author has an hindex of 22, co-authored 110 publications receiving 1843 citations. Previous affiliations of James P. Verdon include University of Cambridge.

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Comparison of geomechanical deformation induced by megatonne-scale CO2 storage at Sleipner, Weyburn, and In Salah

TL;DR: This study examines three large-scale sites where CO2 is injected at rates of ∼1 megatonne/y or more: Sleipner, Weyburn, and In Salah, with particular focus on the risks to storage security posed by geomechanical deformation.
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Linking microseismic event observations with geomechanical models to minimise the risks of storing CO2 in geological formations

TL;DR: In this paper, the authors developed the concept of using observations of micro-seismic activity to help ground truth geomechanical models and found that an alternative model whose reservoir is an order of magnitude softer than lab core-sample measurements provides a much better match with observation, as it leads shear stresses to increase above the production wells, promoting microseismicity in these areas and generates changes in effective horizontal stresses that match well with Swave splitting observations.
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UK public perceptions of shale gas hydraulic fracturing: The role of audience, message and contextual factors on risk perceptions and policy support

TL;DR: In this article, the authors present the first detailed UK experimental survey of public perceptions of shale gas fracking, including analysis of the effects of different messages and the relative influence of different audience, message and contextual factors on support and risk perceptions in respect of the UK fracking.
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A strategy for automated analysis of passive microseismic data to image seismic anisotropy and fracture characteristics

TL;DR: In this article, a workflow strategy for automatic and effective processing of passive microseismic data sets, which are ever increasing in size, is presented, which is based on characteristic differences between the two independent eigenvalue and cross-correlation splitting techniques.