New insight into the microtexture of chalks from NMR analysis

An integrated petrographical and petrophysical study was carried out on a set of 35 outcrop chalk samples, covering a wide range of lithologies and textures. In this study various chalk rock-types have been characterized, in terms of microtextures and porous network, by integrating both geological, sediment-petrological and petrophysical data, including porosity, permeability, low-field NMR (Nuclear Magnetic Resonance), MICP and specific surface area (BET) measurements. The data allow an in depth understanding of the NMR signal of chalks, with a focus on tight chalks, including all low reservoir quality chalks independently of their sedimentological and/or diagenetic history. The study aims to develop an NMR-based approach to characterize a broad range of chalk samples. The provided laboratory low-field NMR chalk classification can be used as a guide to interpret NMR logging data. Based on the petrographical and petrophysical analysis, 6 groups of samples were identified, each of them characterized by a unique NMR signature: (1) micritic chalks, (2) grainy chalks, (3) cemented chalks, (4) marl-seam chalks, (5) argillaceous chalks and (6) silicified chalk. NMR T-2 distributions were linked to pore body size and T2 logarithmic (T-2lm) was calculated. It is apparent that tight chalks, whether their characteristics are sedimentological or diagenetic, yield smaller pore body sizes (T-2lm < 20 ms), as well as narrower pore throats (average radius < 150 nm) and lower permeability values (typically below 0.2 mD). Grainy chalks possess T-2 distributions reflecting larger pore sizes (T-2lm > 60 ms) and pore throats (average radius > 290 nm) and higher permeabilities (up to 13 mD). The marl-seam chalk samples yield bimodal T2 distributions, with a first peak related to the micritic matrix pores and a second peak related to intraparticle pores within fossils. For all samples, permeability was inferred from NMR spectra using SDR (Schlumberger Doll Research) model. (C) 2016 Elsevier Ltd. All rights reserved.