CO2 storage should be able to maintain 99% of CO2 for at least a thousand years. A necessary condition for not having leakage is that the caprock has an adequate sealing capacity, ensured only if the threshold capillary pressure of caprock is larger than the capillary pressure due to the CO2 injection. The threshold capillary pressure is experimentally determined through tests in which the non wetting fluid is forced to penetrate into water saturated specimen. This work reports the results of two threshold tests performed on remoulded caprock specimens using CO2 in supercritical condition and air as non-wetting fluid.

Safe Carbon dioxide capture and storage (CCS) requires characterization of the geochemical interaction between CO2 and caprock at micro and nano scales. Ordovician Kingsport formation limestone rock samples were obtained at a depth of 250 m from Nyrstar Tennessee Mines. The characterization and monitoring of the geochemical and fracture behavior of the rock samples exposed to CO2 brine for a long duration were studied using 3D computed tomography (CT), and high speed coupled micro beam x ray fluorescence (μXRF) and micro beam x ray diffraction (μXRD).

The objective of this work is to characterize synthetic carbonate rocks, mainly composed by calcite, through microtomography and petrography, focusing on a comparative analysis before and after load application and chemical degradation. Dissolution tests were performed in a modified oedometer cell adapted to measure horizontal stress. Dissolution was conducted using an acid solution (10% acetic acid) to evaluate the influence of the pH on the mechanical behaviour of the samples. A model for degradation of carbonatic soft rocks based on critical state theory was used to reproduce the observed behaviour in experiments where mechanical and chemical loads are applied.

This works deals with the development of a reactive transport numerical model, to reproduce the relevant hydro-chemo-mechanical process in calcite-rich caprock in contact with CO2

The goal of this work is to apply and further develop technology based on Supervised Machine Learning algorithms to drastically boost the efficiency of reactive transport models of carbon mineralization through injection of CO2-charged seawater into basaltic rocks.

The stability and serviceability of stress sensitive subsurface reservoirs for large scale long term energy storage can be examined using geomechanical reservoir models. In this work, we adopt a machine learning-based, finite element modelling framework to model the evolution of the macroscopic geomechanical behaviour of a subsurface reservoir. In efforts to ensure model robustness and prediction physical validity, we resort to the application of the joint Thermodynamics based Artificial Neural Networks x Finite Element Modelling (TANNxFEM) framework to geomechanical reservoir modelling. The proposed framework presents a computationally efficient alternative to current reservoir modelling schemes.

In this presentation, we will briefly introduce methods and technologies enabling the subsurface dynamic modelling of CO2 injection and storage. Seismic and borehole petrophysical and geophysical measurements for initial subsurface characterization and consecutive monitoring will be briefly described. These methods and technologies have been applied by SLB in CO2 sequestration projects worldwide. The case of Illinois IBDP project (USA) will be presented and discussed; some perspectives will also be examined regarding near wellbore injectivity evaluation.