ALBERT

Description: Past warm periods provide an opportunity to evaluate climate models under extreme forcing scenarios, in particular high ( 〉  800 ppmv) atmospheric CO2 concentrations. Although a post hoc intercomparison of Eocene ( ∼  50  Ma) climate model simulations and geological data has been carried out previously, models of past high-CO2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4 ×  CO2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP – the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modelling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols. Initial conditions, simulation length, and output variables are also specified. Finally, we explain how the geological data sets, which will be used to evaluate the simulations, will be developed.

Description: Carbonate reservoir rocks exhibit a great variability in texture that directly impacts petrophysical parameters. Many exhibit bi- and multimodal pore networks, with pores ranging from less than 1 μm to several millimeters in diameter. Furthermore, many pore systems are too large to be captured by routine core analysis, and well logs average total porosity over different volumes. Consequently, prediction of carbonate properties from seismic data and log interpretation is still a challenge. In particular, amplitude versus offset classification systems developed for clastic rocks, which are dominated by connected, intergranular, unimodal pore networks, are not applicable to carbonate rocks. Pore geometrical parameters derived from digital image analysis (DIA) of thin sections were recently used to improve the coefficient of determination of velocity and permeability versus porosity. Although this substantially improved the coefficient of determination, no spatial information of the pore space was considered, because DIA parameters were obtained from two-dimensional analyses. Here, we propose a methodology to link local and global pore-space parameters, obtained from three-dimensional (3-D) images, to experimental physical properties of carbonate rocks to improve P-wave velocity and permeability predictions. Results show that applying a combination of porosity, microporosity, and 3-D geometrical parameters to P-wave velocity significantly improves the adjusted coefficient of determination from 0.490 to 0.962. A substantial improvement is also observed in permeability prediction (from 0.668 to 0.948). Both results can be interpreted to reflect a pore geometrical control and pore size control on P-wave velocity and permeability.