Sub-Garn Sands Blog : Part 3 – Petrographic Data Capture

Ensuring consistency, every step of the way

Consistently, accurately, and objectively recording variations grain size, and mineralogical composition through our experienced geologists point counting thin-sections – is key to evidencing the relationships between lithology, diagenesis, and reservoir quality in time equivalent sands across a range of locations and present depths.

Dr. Daniel Atkin & Dr. James Foey are capturing petrographic datasets using the PETROG™ software, with a fully automated stepping stage, integrated with our microscope and camera. Across our 275 samples (i.e., 1 for every 10m of core being described); Daniel & James are alternating between them the responsibility for sample point counts, within each well. This is intended to randomise any pre-determination or natural bias that each geologist may have on what’s important to record. We must limit our perception of any patterns or trends until we collate and interpret the datasets later.

We begin with grain-sizing (i.e., textural analysis); Daniel and James record 100 counts per sample of the long-axis across rigid detrital grains (i.e., quartz, feldspar, lithic fragments), as its these which effect the porosity maintained, when these sands were compacted. Ductiles (i.e., typically mafic igneous fragments rich in olivines, plagioclase feldspar & pyroxene) degrade more easily and are often deformed. We select an AOI within the slide, then PETROG™ sets a snaking pattern, spreading out the number of steps to ensure a representative portion of the AOI is counted. We count every grain that intersects the horizontal crosshair line, adjusting the lens and zoom where required based on the grain size. Oversized grains (e.g., pebbles or granules) can present an issue, but are easily accounted for. With our 100 point counts complete – data is uploaded into an Excel macro, producing a histogram for each sample (an appendix to our report), showing distribution of grain sizes and dominant modal abundance. This allows us to assign samples lithologically to (for example) a very fine sandstone. Thus far, we’ve observed plenty of grain size variation within each well (as opposed to between wells, or between formations), which implies a primary facies control on grain size, as opposed to a provenance control (TBC after the upcoming core-description visits).  

Moving on to modal counts; Daniel and James record 300 point counts per sample within a defined AOI, within which PETROG™ will select points randomly (but evenly spaced). Categories that we’re able to log our observations & identifications from, include detrital grains, bioclastic grains, carbonate grains, authigenic minerals, matrix, porosity and organic minerals. Qualifiers on the recognition of a cement are important, so for example after identifying siderite or chlorite, we’re able to record its relationship to the host lithology (e.g., filling intergranular porosity, or continuous grain coating). Primary porosity is recorded where the stepper reaches a position with the blue resin which may, for example, be a degraded grain. Thus far, what we observed as being important in the Garn are mirrored in the Sub-Garn; chlorite is a major factor inhibiting quartz overgrowths, while those with poor reservoir quality are typically quartz cemented and lacking chlorite. Ferroan dolomite has often been recorded that quite heavily cements sands, in addition to siderite and sideritised detrital clays (especially in the Tilje, being tidal, slack water). Siderite is generally problematic for reservoir quality, though if the replacement of detrital clays (in the methagenesis band of burial/compaction) with siderite is early enough it can help resist compaction. In our work so far, chlorite is mostly radial pore-lining cement (beneficial for reservoir quality), though in places is also pore filling. We have also observed illite cements, tending to help retain but reduce porosity, isolating them (i.e., reducing connectivity/permeability), make porosity less effective.