Generating actionable intelligence from drill cuttings using big data handling of Automated Mineralogical analysis.
Marc Enter, Matthew Power
Automated Mineralogy is a premium lab-based mineralogical and textural analysis. A fully quantitative mineralogical measurement it forms the gold standard by which other data are calibrated. It is the automated mapping of rock samples using a scanning electron microscope (SEM) to collect data from a backscattered electron (BSE) detector and energy dispersive spectrometers (EDS). The result is quantitative mineralogical abundance, provided in the context of a picture or ‘mineral map’.
The mineral map provides the key dimension for the analysis of particulate mediums such as drill cuttings or RC chips since each particle can be evaluated individually. This affords a level of detail that was previously not possible using bulk analysis techniques and point sampling workflows.
Using a designation process based on mineralogical composition and textural features, each particle can be assigned a ‘lithotype’. Outputs from Automated Mineralogical analysis including modal mineralogical abundance, grain density, macroporosity and mineral size, can be reported for each individual particle and ultimately for each lithotype.
While generating over a thousand measured variables per drill cuttings sample offers a high degree of detail about the sample, this amount of data is unwieldy and can make it difficult to deduce observations when comparing individual geological units or variations from well to well.
Modern big data handling techniques allow for data to be drilled down enabling the deduction of key parameters in lithotypes and for the comparison of variables throughout a reservoir or ore body.
The author will present examples of actionable intelligence such as the digital removal of drilling contaminants, the identification and removal of cavings, the deduction of a key lithotype within a reservoir and how different wells compare in terms of this key lithotype.
The analysis of drill cuttings and RC chips provides a low-cost opportunity for the future of geoanalysis. Modern big data handling techniques such as machine learning and the widespread adoption of domain specific programming languages like Python make the combination of particulate mediums and Automated Mineralogy an exciting space in the resources sector.
