Post-doc position on mechanical and hydrological processes in soil at Westlake University

Introduction of The Lab/ Research Field

Dr. Lei’s research on porous media features temperature, pressure and stress control, pore-scale insights based on micro-CT technique integrated with conventional testing, multi-physics experiments and theoretical analysis. Targeted areas include: (1) particulate material and porous media under abnormal conditions and their interaction with pore constituents; (2) energy, marine, and resource geo-engineering; (3) permafrost and outer space sediments; (4) natural gas hydrates and their hosting sediments; (5) massive energy storage; and (6) multi-phase flow and crystallization in porous media.

Website of Digital Porous Media Laboratory: dpml.westlake.edu.cn

Dr. Lei’s webpage: https://en.westlake.edu.cn/academics/School_of_Engineering/About/Our_People/Faculty/202006/t20200611_3903.shtml

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Post-doc position on permafrost/frozen ground at Westlake University

This is a post-doc position in Liang Lei’s lab for a joint permafrost project collaborating with Ling Li’s lab and Sergio Andres Galindo Torres’s lab.

Introduction of The Lab/Research Field

Dr. Lei’s research on porous media features temperature, pressure and stress control, pore-scale insights based on micro-CT technique integrated with conventional testing, multi-physics experiments and theoretical analysis. Targeted areas include: (1) particulate material and porous media under abnormal conditions and their interaction with pore constituents; (2) energy, marine, and resource geo-engineering; (3) permafrost and outer space sediments; (4) natural gas hydrates and their hosting sediments; (5) massive energy storage; and (6) multi-phase flow and crystallization in porous media.

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PhD opportunity: “Experimental and numerical investigations of puncture failure of geomembranes lining systems”

Laboratory experiments dealing with interactions between granular materials and geomembranes will be conducted in the project. Then DEM modeling of the experiments will be designed following a “digital twin” approach. The objective of the PhD is to precise the local processes of geomembrane failure subjected to puncturing actions and precise the design of protective layers of geotextiles. More information in the attached proposals (french and english).

The PhD project will be conducted at INRAE in Aix-en-Provence (France) and may start as early as October 2021 (with some flexibility).

Applications (CV and cover letter) should be sent to Guillaume Stoltz (guillaume.stoltz@inrae.fr) and Antoine Wautier (antoine.wautier@inrae.fr).

Position: ATER at Centrale Nantes

L’École Centrale Nantes recherche un.e Attaché.e Temporaire d’Enseignement et de Recherche pour l’équipe de Géomécanique – Génie Civil.

Activités d’enseignement :

Le (la) candidat(e) devra enseigner dans les matières liées au Génie Civil et à la Mécanique des géomatériaux. Il (elle) interviendra notamment dans les différents Unités d’Enseignement liées avec la Géomécanique/Géotechnique et en général le Génie Civil dans les différents parcours de la formation Génie Civil au sein de Centrale Nantes (ingénieur (sous statuts étudiant et apprenti), master). La capacité d’enseigner en anglais est un plus. Le (la) candidat(e) retenu(e) participera à la vie de l’École et de l’équipe pédagogique.

Activités de recherche :

Le (la) candidat(e) mènera ses activités de recherche au sein de l’Equipe Matériaux – Environnement Ouvrages – du GeM.

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PhD Opportunity: “Development of novel grouting technology for subsurface engineering & the net-zero carbon revolution”

The University of Strathclyde is currently recruiting for a funded PhD project in subsurface engineering.

This lab-based project will focus on developing micron- and nano-scale technology to create novel grouts for enhancing the hydro-mechanical properties of the subsurface. During the project, different subsurface engineering applications will be scoped with the aim of targeting net-zero carbon related opportunities such as energy storage (compressed air, hydrogen), geothermal energy, and carbon sequestration. The candidate will be joining a research group with a track record in developing novel grouts, and in using advanced techniques such as X-ray micro CT and bespoke designed mechanical testing equipment to characterise and evaluate the grout effectiveness.

The deadline for the application is the 13 August 2021. For more details or information on how to apply, please see this file.

Post-doc Position at the University of Southampton (UK): SEAMLESS ‘SharEd Anchor Multidirectional Load Envelopes with Strength Synthesis’

We are hiring a post-doc for a 12months project on shared anchors for floating offshore renewable energy technologies. The project will systematically investigate the cyclic behaviour of those anchors by geotechnical centrifuge modelling in the new National Infrastructure Laboratory. Centrifuge tests will be complemented by X-ray tomography to investigate the anchor failure mechanism. This is an excellent opportunity to work closely with the offshore industry on the future of offshore engineering, as the project has several industrial partners (NGI, EQUINOR, CORPOWER, Lloyds Register and ORE CATAPULT).

More details can be found in this file or on the site of the University of Southampton:

https://jobs.soton.ac.uk/Vacancy.aspx?ref=1416021DA

Joint PhD position at the CY Cergy Paris Université (France) and the University of Warwick (UK)

PhD Studentship: REcycling of waste geomaterials and their application as primary
constituents for high-level CONstructions (RECON)

We are looking for an enthusiastic and highly motivated PhD student with proven interest and background in geomechanics and/or construction materials fields. This is an exciting opportunity to be part of a joint PhD research supported by the EUTOPIA PhD Cotutelle programme. The PhD study will be carried out at the CY Cergy Paris Université (CY) in France and the University of Warwick (UoW) in the UK. The doctoral student will spend half of the PhD in CY and half of the PhD in UoW.

More information in this attachment.

PhD position at Heriot-Watt University (UK)

PHD PROJECT:  UNCERTAINTY QUANTIFICATION OF GEOMECHANICALLY SENSITIVE RESERVOIRS USING PHYSICS-INFUSED MACHINE LEARNING

What is the problem?

When we use the subsurface to store CO2 or Hydrogen, the rocks can deform inelastically as the reservoir conditions change – processes well described in geomechanics. But this permanent strain can lead to significant leakage and to induced seismicity risks during operation and/or long term storage. At worst, these risks pose a substantial threat to property and health – so much so that regulators and investors may halt projects before they start as we can’t correctly estimate the geomechanically-derived risks.

The key technical challenge for most storage reservoirs is to accurately quantify uncertainties and risks associated with geomechanical property changes from a few computer model forecasts. Geomechanical simulation software is complex and data-intensive, each run taking many hours to days. Such long run times make statistically thorough methods to quantifying uncertainty impractical. In most circumstances, we can’t afford the thousands of required model realisations. So such risks may be misestimated or even missed.

The longest model run times occur when we couple simulations of fluid flow (production and injection) with geomechanical simulations to predict how a development plan may alter the reservoir rocks properties and how this will impact fluid movement and field operations. To solve the system of equations for fluid flow and geomechanics together, we need to connect very different modelling approaches, the differences in the solvers typically precluding full coupling. Instead, the packages interact by running separately but simultaneously and passing data back and forth between iterations. This is a technically monumental challenge and the run times of the models are far longer than the combined times of each model run separately.

To accurately quantify uncertainties in geomechanically sensitive reservoirs we must run many more models than we do today, exploring a more diverse set of geological scenarios. But to run more models, we must significantly improve the efficiency of coupling fluid flow and geomechanical simulations. Machine learning provides one solution: once trained on an appropriate data set it can capture complex, non-linear systems very rapidly.

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