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Offer for a Thesis AllowanceSubject: Effect of organic nanoporosity on CO2 Geological sequestration from molecular and multi-scale simulations
From October 24, 2018 to January 1, 2019
Thesis subject
The central characteristic underlying CO2 geological sequestration is the large adsorption capacity of organic source rocks. This is mainly due to the coal matrix (coalbed reservoirs)...Postdoctoral positionMolecular dynamics investigation of Non-Fickian effects on geological sequestration of CO2
From October 24, 2018 to January 1, 2019
The central characteristic underlying CO2 geological sequestration is the large adsorption capacity of organic source rocks. This is mainly due to the coal matrix (coalbed reservoirs) or kerogen (shales’ reservoirs), the bulk constituant of the unconventionnal reservoirs considered for CO2 sequestration.
Indeed, this phase, mainly composed of carbon, is nanoporous with a large pore surface to volume ratio due to the large presence of an amorphous and microporous phase (pore size < 2 nm). The fluids (hydrocarbon, CO2) adsorbed in such materials are subject to strong adsorption effects drastically altering their thermophysical properties compared to their bulk states. The solid-fluid couplings cannot be neglected in this case and can be for instance responsible of the swelling of coal samples upon adsorption of CO2.
Thanks to recent experimental and numerical techniques, we now start to have access to morphological and textural informations at the micro- and meso-scopic scales of the bulk organic matter of geological reservoirs. Realistic atomistic representations of the microporous phase from various shale origins and locations can be obtained by Molecular Dynamics (MD) simulations [1,2].Postdoctoral position in Environmental Sciences
From October 2, 2018 to October 31, 2018
Almost every area of human endeavour has been impacted in the past few decades by new forms of data. An immediate example is ‘big data’. In addition to a greater volume of data from traditional sources, there are also many more sources of potentially useful data. For example, traditional observational data can be more easily acquired using new technology, and can be complemented by new digital data acquired by sensors such as satellites, wearables and automatic monitoring devices. Complex data can also include ‘systems data’ which require a fusion of data sources to comprehensively describe a complicated process.
Bayesian computational statistics offers an appealing framework for modelling and analysis of all of these forms of complex data. For example, models that encourage sparsity and adaptive sampling can help to address the problem of big data; informative priors can be employed to support little data; and Bayesian networks are an effective way of describing complicated systems.
