Impacts of Mineralogy on Petrophysical Properties

Because of their extreme heterogeneity at multiple scales, carbonate rocks present a great challenge for studying and managing oil reservoirs. Depositional processes and diagenetic alterations of carbonates may have produced very complex pore structures and, consequently, variable fluid storage and flow properties of hydrocarbon reservoirs. To understand the impact of mineralogy on the pore system, we analyzed four carbonate rock samples (coquinas) from the Morro do Chaves Formation in Brazil. For this study, we used thin sections and XRD for their mineralogical characterization, together with routine core analysis, NMR, MICP and microCT for the petrophysical characterizations. The samples revealed very similar porosity values but considerably different permeabilities. Samples with a relatively high quartz content (terrigenous material) generally had lower permeabilities, mostly caused by more mineral fragmentation. Samples with little or no quartz in turn exhibited high permeabilities due to less fragmentation and more diagenetic actions (e.g., dissolution of shells). Results confirm that carbonate minerals are very susceptible to diagenesis, leading to modifications in their pore body and pore throat sizes, and creating pores classified as moldic and vug pores, or even clogging them. For one of the samples, we acquired detailed pore skeleton information based on microCT images to obtain a more complete understanding of its structural characteristics

Magnetic reconance imaging characterization of porous substrates and models of soil and biofilms

Bacteria and biofilms are frequently found growing in sponges and are a common cause of malodour in the sponge and cross-contamination of humans and surfaces with the risk of causing bacterial infections. Bacterial infection generates high annual healthcare costs and are related to high mortality rates. Removal of bacteria and biofilms from sponges is thus of critical importance to avoid infections, food poisoning, and surface contamination. To tackle biofilm development in sponges, and thus reduce cross contamination, fast-moving consumer goods (FMCGs) companies have focused on developing detergents which can remove food from sponges. However, due to their intrinsic method’s limitation, current existing techniques, such as X-Ray micro-CT, confocal laser scanning microscopy, scanning and transmission electron microscopy, do not allow a complete understanding and visualization on how detergents act in removing food and biofilms from sponges. To fulfil this gap, new methodologies need to be developed to increase knowledge on biofilm development, particularly within sponges, and its relationship with food residues, as well as on fluid dynamics within sponges. Thanks to their nature, nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) could allow to image food residues and biofilm development in porous media in-situ and in-vivo. Moreover, NRM and MRI enable the characterization of the 3D internal structure of porous media and the visualization of fluid flow within them. Thus, NMR and MRI could potentially allow to study how the physical parameters of sponges affect fluid flow, and how this is related to food residues removal and biofilm development in sponges, in-situ and without the need of pre-treatments. In this work, novel nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) have been developed to characterize polyurethane open-cell porous media, and directly visualize food and biofilms within, without the need of sample pre-treatment or the addition of contrast agents. A combination of NMR relaxometry, MRI T2 relaxation maps and X-ray micro computed tomography (µCT), has highlighted the dependence of T2 relaxation times of water, within the foam, on the pore sizes. This has enabled the creation of new protocols to characterize polymeric open-cell porous media directly by MRI, which can be combined with MRI visualization of composition and flow. MRI Chemical Shift Selective (CHESS) imaging has been employed to visualize food residues within polyurethane (PU) sponges, enabling the selective mapping of hydrophobic and hydrophilic residues within the sponge without additional contrast agent. Finally, MRI chemical exchange saturation transfer (CEST) experiments have been successfully developed to image polysaccharide-based (alginate) materials within sponges, demonstrating their potential to visualise the exopolysaccharide matrix of biofilms without the need for additional contrast agents

Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an  activation function recently proposed in the literature is implemented and tested. The  computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning