Thermo-hydro-mechanical analysis of CO2 injection into deep aquifers

One of the biggest challenges for humanity is global warming and consequently, climate changes. Even though there has been increasing public awareness and investments from numerous countries concerning renewable energies, fossil fuels are and will continue to be in the near future, the main source of energy. Carbon capture and storage (CCS) is believed to be a serious measure to mitigate CO2 concentration. CCS briefly consists of capturing CO2 from the atmosphere or stationary emission sources and transporting and storing it via mineral carbonation, in oceans or geological media. The latter is referred to as carbon capture and geological storage (CCGS) and is considered to be the most promising of all solutions. Generally it consists of a storage (e.g. depleted oil reservoirs and deep saline aquifers) and sealing (commonly termed caprock in the oil industry) formations. The present study concerns the injection of CO2 into deep aquifers and regardless injection conditions, temperature gradients between carbon dioxide and the storage formation are likely to occur. Should the CO2 temperature be lower than the storage formation, a contractive behaviour of the reservoir and caprock is expected. The latter can result in the opening of new paths or re-opening of fractures, favouring leakage and compromising the CCGS project. During CO2 injection, coupled thermo-hydro-mechanical phenomena occur, which due to their complexity, hamper the assessment of each relative influence. For this purpose, several analyses were carried out in order to evaluate their influences but focusing on the thermal contractive behaviour. It was finally concluded that depending on mechanical and thermal properties of the pair aquifer-seal, the sealing caprock can undergo significant decreases in effective stress.

Comments on ‘‘Modeling fractional stochastic systems as non-random fractional dynamics driven Brownian motions

Applied Mathematical Modelling, Vol.33

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15th IEEE International Conference on Electronics, Circuits and Systems, Malta

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The last three decades have seen quite dramatic changes the way we modeled time dependent data. Linear processes have been in the center stage in modeling time series. As far as the second order properties are concerned, the theory and the methodology are very adequate.However, there are more and more evidences that linear models are not sufficiently flexible and rich enough for modeling purposes and that failure to account for non-linearities can be very misleading and have undesired consequences.

A score test for non-nested hypotheses with applications to discrete data models

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