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Collection of data from the PhD Thesis "Thermo-mechanical loading of intact rock and discontinuities" by J Woodman. This collection of data includes raw logged .csv datafiles of uniaxial compression testing and triaxial compression testing on intact synthetic compositions, as well as intact and discontinuous specimens of Thornhill Rock and Midgley Grit at both ambient temperatures, and temperatures up to 100°C.
These data contain time series of stress, strain, confining pressure, pore pressure, pore volume, permeability and elastic wave velocities of samples of Purbeck Limestone deformed under hydrostatic and triaxial conditions at room temperature. All samples were saturated with decane as pore fluid.This dataset is used and fully described/interpreted in the paper: Brantut, N., M. Baker, L. N. Hansen and P. Baud, Microstructural control of physical properties during deformation of porous limestone, submitted to J. Geophys. Res.
This collection comprises two time-series of 3D in-situ synchrotron x-ray microtomography (μCT) volumes showing two Ailsa Craig micro-granite samples (ACfresh02 and ACHT01) undergoing triaxial deformation. These data were collected in-situ at the PSICHE beamline at the SOLEIL synchrotron, Gif-sur-Yvette, France in December 2016 (standard proposal 20160434) and are fully explained in Cartwright-Taylor A., Main, I.G., Butler, I.B., Fusseis, F., Flynn M. and King, A. (in press), Catastrophic failure: how and when? Insights from 4D in-situ x-ray micro-tomography, J. Geophys. Res. Solid Earth. Together, these two time-series show the influence of heterogeneity on the micro-crack network evolution. Ailsa Craig micro-granite is known for being virtually crack-free. One sample (ACfresh02) remained as-received from the quarry until it was deformed, while the second (ACHT01) was slowly heated to 600 degC and then slowly cooled prior to deformation in order to introduce material disorder in the form of a network of nano-scale thermal cracks. Thus these two samples represent two extreme end-members: (i) ACfresh02 with the lowest possible (to our knowledge) natural pre-existing crack density, and so is a relatively homogeneous sample and (ii) ACHT01 with a thermally-induced nano-crack network imprinted over the nominally crack-free microstructure, and therefore has increased heterogeneity relative to ACfresh02. Each 3D μCT volume shows the sub-region of each sample in which the majority of damage was located and has three parts. Part one is reconstructed 16-bit greyscale data. Part two is 8-bit binary data showing individual voids (pores and micro-cracks) in the dataset after segmentation. Part three is 32-bit data showing the local thickness of each void, as in Cartwright-Taylor et al. (in press) Figures 4 and 5. Each part is a zip file containing a sequence of 2D image files (.tif), sequentially numbered according to the depth (in pixels, parallel to the loading axis) at which it lies within the sample volume. File dimensions are in pixels (2D), with an edge length of 2.7 microns. Each zip file is labelled with the sample name, the relevant letter for each 3D volume as given in Cartwright-Taylor et al. (in press) Tables 3 and 4, part 1, 2 or 3 (depending whether the data are greyscale, binary or local thickness respectively), the differential stress (MPa) on the sample, and the associated ram pressure (bar) to link with individual file names. The following convention is used: sample_letter_part_differentialstress_rampressure_datatype. Also included are (i) two spreadsheets (.xlsx), one for each sample, containing processing parameters and the mechanical stress and strain at which each volume was scanned, and (ii) zip files containing .csv files containing measurement data for the labelled voids in each volume. N.B. void label numbers are not consistent between volumes so they can only be used to obtain global statistics, not to track individual voids.
P* data obtained through hydrostatic loading experiments, using triaxial experimental apparatus, as well as yield curve data obtained through differential loading tests, prior to the discovery of P* for different synthetic sandstones. The methodology used was taken from Bedford et al. (2018, 2019). Grain size analysis data obtained using a Beckman Coulter LS 13 320 laser diffraction particle size analyser. Particle analysis was conducted on five different synthetic sandstones with different grain size distributions. Secondary electron and backscatter electron SEM images for natural and synthetic sandstones. Secondary electron images were stitched together to form a whole core image. They were then binarised following the methodology of Rabbani and Ayatollahi. (2015). Hexagon grid size data used to obtain the correct grid size for performing porosity analysis across an mage using Fiji software (Brown, 2000). Bedford, J. D., Faulkner, D. R., Leclère, H., & Wheeler, J. (2018). High-Resolution Mapping of Yield Curve Shape and Evolution for Porous Rock: The Effect of Inelastic Compaction on 476 Porous Bassanite. Journal of Geophysical Research: Solid Earth, 123(2), 1217–1234. Bedford, J. D., Faulkner, D. R., Wheeler, J., & Leclère, H. (2019). High-resolution mapping of yield curve shape and evolution for high porosity sandstone. Journal of Geophysical Research: Solid Earth. Brown, G. O., Hsieh, H. T., & Lucero, D. A. (2000). Evaluation of laboratory dolomite core sample size using representative elementary volume concepts. Water Resources Research, 36(5), 484 1199–1207. Rabbani, A., & Ayatollahi, S. (2015). Comparing three image processing algorithms to estimate the grain-size distribution of porous rocks from binary 2D images and sensitivity analysis of the grain overlapping degree. Special Topics & Reviews in Porous Media: An International Journal, 6(1).
A brief description of ten core plug samples collected from borehole GGC01 (Glasgow, United Kingdom) is provided, as well as for twelve 15-50 g cuttings samples from 1m intervals within borehole GGA08. Samples were also collected from borehole GGA02 but not used for further analyses. Data comprise results from geomechanical tests, permeability and porosity measurements, and X-Ray Diffraction analyses performed on drillcore samples of sandstones, siltstones, mudstones and coals from eleven depth intervals within the GGC01 borehole. Geomechanical data include triaxial compressional strength, tensile strength, and frictional strength. Frictional strength data was also collected for cuttings samples of sandstones, siltstones, mudstones and coals from the GGA08 borehole. In total twenty-three tensile strength tests were performed on ten sampled intervals, and seven porosity measurements pre-and post-failure were taken. Nine triaxial compressive strength tests and twenty-one frictional strength tests were performed, with permeability measured both before and after failure or shear respectively. From compressive strength tests we also determined the Young’s modulus and Poisson’s ratio. Samples and data are derived from the UK Geoenergy Observatories Programme funded by the UKRI Natural Environment Research Council and delivered by the British Geological Survey.
Friction coefficient and frictional stability (rate & state parameter) data for triaxially compressed direct shear experiments on kaolinite-rich china clay and Mg-montmorillonite fault gouges (<2micron grain size). A total of 19 raw experimental datasets are presented as detailed in the index files: 13 on kaolinite-rich china clay, and 6 on cation-exchanged Mg-Montmorillonite. The raw data files, logged at either 1 or 2Hz, comprise confining pressures, upstream and downstream fluid pressures, force experienced by the direct shear assembly during triaxial compression, and absolute volumes of the confining pressure and fluid pressure reservoirs. Data is provided as measured by gauges in the pressure vessel in Volts, and also as calculated in MPa, kN and mm3. Also presented are the outputs of MATLAB models run to simulate the rate and state parameters k, a, b, dc and f0 for each experiment, with error data presented as 2sigma and standard error values. Parameters were determined using a non-linear least-squares fitting routine with the machine stiffness treated as a fitting parameter (c.f. Noda and Shimamoto, 2009). Data were fit by a single set of state variables (a, b, dc) with a linear detrend. Also presented are the outputs of Specific Thermogravimetric Analyses on kaolinite-rich china clay and Mg-montmorillonite.