As conventional plays decline and exploration targets shift to shale, tight oil and other difficult reservoirs, geoscientists are being asked to do more with less: produce reliable mineralogical data faster, at lower cost and with greater accuracy. Here, Salomé Larmier, PhD holder in oil and gas geology and electron microscopy specialist at Thermo Fisher Scientific, explains how automated mineralogy systems can enable more efficient resource extraction in the oil and gas industry.
Importance of Mineral Analysis in Oil and Gas Exploration
Mineral analysis has long been a critical part of oil and gas exploration. Before drilling programs are designed or stimulation methods selected, understanding the mineral composition of subsurface rock is essential. The relative abundance of clays, carbonates, quartz and organic material influences key reservoir characteristics such as porosity, permeability and mechanical behaviour, all of which can determine how a formation will perform during production.
Challenges in Mineralogical Analysis for Unconventional Reservoirs
In both conventional and unconventional plays, accurate mineral characterisation reduces uncertainty and supports more effective decision-making across the exploration and production lifecycle. However, as exploration moves into increasingly complex geological environments, obtaining this level of insight has become much more demanding.
Unconventional reservoirs introduce significant challenges for mineralogical analysis. Shale reservoirs, for example, are not only fine-grained and heterogeneous, but they often contain mineral intergrowths, boundary phases and micro-scale variations that complicate accurate analysis.
Limitations of Traditional Mineralogy Techniques
To address these complexities, geoscientists have relied on a combination of methods such as X-ray diffraction (XRD), scanning electron microscope (SEM) imaging and bulk chemical assays. Each provides valuable insight but also comes with constraints. XRD can identify crystalline phases but often lacks the spatial resolution to resolve fine-scale textures or phase boundaries. Traditional SEM imaging provides structural detail but typically requires manual interpretation and lacks the throughput needed for large sample sets. Bulk assays, meanwhile, can obscure the heterogeneity of the sample, making it difficult to correlate chemical data with specific mineral phases or microstructures.
As a result, obtaining a high-resolution, spatially resolved and compositionally accurate picture of a reservoir rock often requires integrating multiple datasets — a time-consuming and resource-intensive process that can introduce inconsistencies between workflows and across teams.
Advantages of Automated Mineralogy Systems in Reservoir Analysis
To overcome these limitations, many exploration teams are turning to automated mineralogy systems — a method that combines high-resolution imaging with compositional data to deliver more comprehensive mineral characterisation. These systems integrate SEM with energy-dispersive X-ray spectroscopy (EDS) to map mineral phases across a sample with far greater detail and consistency than traditional workflows allow.
One of the more advanced capabilities comes from how these systems identify mineral phases. In traditional mineral analysis, each pixel or analysis point is typically assigned a single dominant mineral phase, even if the underlying material is a mix of different minerals. Unlike manual methods, automated mineralogy systems software can classify minerals at the pixel level, even in fine-grained or mixed-phase samples.
Innovative Software and Algorithm Use in Mineral Identification
For example, the Maps Min Software developed by Thermo Fisher Scientific uses an advanced algorithm, Mixel, to detect and quantify multiple mineral phases at each point. Rather than being constrained to a single-phase assignment, Mixel treats each spectrum as potentially containing contributions from multiple mineral phases. This algorithm can process mixed spectra automatically, determining both the mineral phases present and their relative proportions with high accuracy.
This is particularly useful for formations like shale, where mineral intergrowths and cryptocrystalline textures are common. By accurately characterising complex mineral assemblages and linking them to critical rock properties, automated mineralogy systems software enables a clearer understanding of reservoir quality.
Practical Application in Shale Exploration and Operational Workflows
In a typical shale exploration scenario, an automated mineralogy system might be used to evaluate the clay composition along a core section. Certain clay types, such as smectite, are prone to swelling and can compromise wellbore stability or reduce permeability after hydraulic stimulation. The ability to rapidly identify these mineral phases allows drilling and completion engineers to adjust fluid designs or alter stimulation plans before potential problems arise. In this way, mineralogical data — once confined to specialist labs — can now feed directly into operational workflows.
As geoscientists increasingly turn to unconventional plays, obtaining accurate and nuanced mineralogical data becomes more and more challenging. However, automated mineralogy systems hold promise in transforming mineral characterisation. By providing high-resolution, spatially resolved data with high accuracy and efficiency, advanced software enables precise characterisation of complex reservoirs, helping geoscientists to make faster, better-informed decisions in the field.
