Characterization Prediction And Modelling Of The Crustal Present Day In Situ Stresses

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Characterization, Prediction and Modelling of the Crustal Present-Day In-Situ Stresses

Characterization, Prediction and Modelling of the Crustal Present-Day In-Situ Stresses
Author :
Publisher : Geological Society of London
Total Pages : 272
Release :
ISBN-10 : 9781786206145
ISBN-13 : 1786206145
Rating : 4/5 (145 Downloads)

Book Synopsis Characterization, Prediction and Modelling of the Crustal Present-Day In-Situ Stresses by : R. Goteti

Download or read book Characterization, Prediction and Modelling of the Crustal Present-Day In-Situ Stresses written by R. Goteti and published by Geological Society of London. This book was released on 2024-08-01 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geomechanics has a marked impact on the safe and sustainable use of the subsurface. Along with an ongoing demand for hydrocarbon resources there is also a growing emphasis on sustainable subsurface exploitation and development, storage of carbon, hydrogen, energy and (radioactive) waste, as well as sustainable geothermal resource utilization. Such activities are accompanied by an ever-increasing need for higher resolution, fit-for-purpose solutions, workflows and approaches to constrain present-day subsurface stresses and minimize associated uncertainties. Building high fidelity geomechanical-numerical models provides critical input and understanding for diverse engineering designs and construction as well as geoscience applications. Such models greatly contribute towards uncertainty reduction, risk management and risk mitigation during the operational life of a given subsurface development and associated infrastructures (both on and below the surface). This Special Publication contains contributions detailing the latest efforts and perspectives in present-day in-situ stress characterization, prediction and modelling from the borehole to plate-tectonic scale. There is particular emphasis on the uncertainties that are often associated with data and models.


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