Abstract Chlorite is a key mineral in the control of reservoir quality in many siliciclastic rocks. In deeply buried reservoirs, chlorite coats on sand grains prevent the growth of quartz cements and lead to anomalously good reservoir quality. By contrast, an excess of chlorite – for example, in clay-rich siltstone and sandstone – leads to blocked pore throats and very low permeability. Determining which compositional type is present, how it occurs spatially, and quantifying the many and varied habits of chlorite that are of commercial importance remains a challenge. With the advent of automated techniques based on scanning electron microscopy (SEM), it is possible to provide instant phase identification and mapping of entire thin sections of rock. The resulting quantitative mineralogy and rock fabric data can be compared with well logs and core analysis data. We present here a completely novel Quantitative Evaluation of Minerals by SCANning electron microscopy (QEMSCAN®) SEM–energy-dispersive spectrometry (EDS) methodology to differentiate, quantify and image 11 different compositional types of chlorite based on Fe : Mg ratios using thin sections of rocks and grain mounts of cuttings or loose sediment. No other analytical technique, or combination of techniques, is capable of easily quantifying and imaging different compositional types of chlorite. Here we present examples of chlorite from seven different geological settings analysed using QEMSCAN® SEM–EDS. By illustrating the reliability of identification under automated analysis, and the ability to capture realistic textures in a fully digital format, we can clearly visualize the various forms of chlorite. This new approach has led to the creation of a digital chlorite library, in which we have co-registered optical and SEM-based images, and validated the mineral identification with complimentary techniques such as X-ray diffraction. This new methodology will be of interest and use to all those concerned with the identification and formation of chlorite in sandstones and the effects that diagenetic chlorite growth may have had on reservoir quality. The same approach may be adopted for other minerals (e.g. carbonates) with major element compositional variability that may influence the porosity and permeability of sandstone reservoirs.

Abstract QemSCAN is a scanning electron microscope (SEM) system, initially designed to support the mining industry by providing rapid automated quantitative mineral analyses. The system is based upon Carl Zeiss SEMs fitted with up to four light-element energy dispersive X-ray spectrometers. Representative subsamples are mounted into either resin or wax blocks and polished prior to analysis, or can be mounted onto carbon tape. During analysis, X-ray spectra are collected at a user-defined pixel spacing and are acquired very rapidly ( c. 10 ms per pixel). The measured spectra are automatically compared against a database of known spectra and a mineral or phase name is assigned to each measurement point by the QemSCAN computer software programs. In this way the near-surface qualitative elemental composition of each particle is systematically mapped, assigned to a mineral name or chemical compound/species, and digital pixel maps of each particle are created. Depending upon a range of parameters, including the particle size and the user-defined pixel spacing (which can vary between 0.20 μm and 25 μm), approximately 1000 particles, each 1–10 μm in size, can be measured per hour using a 1 μm pixel spacing. In addition to providing a qualitative elemental analysis and mineralogical or phase assignment for each particle, data relating to particle size, shape and calculated specific density are also generated. In this study, the potential application of this automated SEM system in forensic geoscience was evaluated by the analysis of: (1) a series of soil samples, and (2) a series of dust samples from an industrial complex. In both case studies, the mine-ralogy/phase composition of each sample analysed was found to be distinctive. In addition, textural data for the soil samples and particle shape data for the dust samples show that they can be clearly distinguished. Automated SEM using QemSCAN has clear potential application in the analysis of soil or other trace evidence in forensic case work.