In general, minicore permeabilities range from 10–17 to 10–14 m2 and porosities were relatively high (Fig. F3A, F3B; Table T1). The values cited in this study are representative of similar permeability and porosity measurements from the PACMANUS hydrothermal system (Christiansen and Iturrino, this volume). The effect of pressure on both the measured permeabilities and electrical resistivities was small (Fig. F3A, F3C), except in the softer and more fragile samples where permanent deformation occurred. Deformation is apparent in samples where there is a significant deviation in measurements for equivalent increasing and decreasing pressure intervals (Fig. F3A; Table T1). The variations in electrical resistivity seem to be related to variations in porosity, where highly porous samples tend to be more conductive (Table T1).

A comparison of digital core images with minicore slice scans and 3-D X-ray tomography reconstructions shows clear evidence for variations in mineralogy, size and distribution of vesicles, vein distributions and orientations, and patchy alteration features (Fig. F1). Color renditions of 3-D X-ray tomography reconstructions also show distinct variations in distribution of dense phases and voids (Fig. F2). In some cases, the 3-D void renditions seem to correlate with porosity values obtained from minicore wet and dry weights and volumes, whereas in other instances, the porosity values seem low relative to the 3-D X-ray tomography void renditions. These variations are typically due to the range of pore sizes, as discussed above: large pores are opaque, whereas smaller pores will be partially or fully transparent.

A particularly interesting case is presented by Sample 193-1188A-3R-1, 13–15 cm, where the minicore porosity value is low and both the visual void rendition and permeability are high (Fig. F2). The grain and bulk density values show small variance (Table T1), supporting the low porosity and high electrical resistivity measurements. The CT visualization is somewhat deceptive, as the voids are large and open and thus opaque, and there are enough of them to seem to cover the sample space. Calculations from the CT data for the uninfiltrated minicore show that the vesicles compose ~5% of the sample by volume, but that many of these are not connected with the outside of the sample and were not filled when the sample was saturated for permeability and CT analysis. Examination of the CT data for the infiltrated sample reveals that its very high permeability can be traced to a single open conduit of connected vesicles that links the top and bottom surfaces of the minicore. Although this conduit is volumetrically small, it is wide enough that it offers comparatively little resistance to flow. However, it should also be noted that this conduit must have eventually terminated within the larger rock volume from which it was cut, and that if a larger sample without through-going conduits had been tested for permeability, presumably the results would have been different.

Sample 193-1188A-9R-1, 118–120 cm, is another case in which seemingly unusual porosity/permeability determinations may be explained by CT data. The permeability is lower than expected given the measured porosity, as compared with other samples in the suite that have lower porosities and higher permeabilities. Examination of the 3-D visualization from the minicore shows that there are several sulfide veins surrounded by quartz, which were shown by two-stage scanning to be largely impermeable and thus may create effective barriers to flow. Two of these veins crosscut much of the minicore sample, likely causing a bottleneck during permeability determination and lowering measured values. The CT data for the corresponding half-round sample shows that these veins are distributed heterogeneously throughout the larger specimen, suggesting that the permeability in this lithology is locally variable, and that the determination for the minicore sample may be unrepresentative.

Significant variations in bright phases are observed in the 3-D X-ray tomography images, suggesting the potential for mineral identification and measurement. However, no direct mineral analyses on cores have been made at this point to verify the accuracy of these images or to identify the particular phases imaged in each sample. Mineral identification in CT data typically relies on some prior knowledge of the phases expected to be present (e.g., Carlson and Denison, 1992; Orsi et al., 1994; Tivey, 1998), although in some cases dual-energy scanning can be used to derive atomic number information, giving additional clues as to composition (Van Geet et al., 2000). If phases or voids can be successfully identified and segmented within the CT data volume, specialized software can be employed to obtain 3-D measurements of size, shape, distribution, and orientation (Kyle and Ketcham, 2004, with supplementary material available at; Proussevitch and Sahagian, 2001).