Linking Geochemical and Mechanical Properties of Rock Samples Using New Non-Destructive Techniques

Abstract

Geochemical analysis of rocks is fundamental to the understanding of geology and earth sciences. X-ray dispersive spectrometry and other automated techniques are increasingly being used to determined and quantify the abundances of the major, trace elements and other rock properties. This study utilized a combination of dispersive spectrometric techniques (MicroXRF) and impulse rebound hammer method to establish links between geochemical and mechanical properties of rocks through a non-destructive method. MicroXRF has high resolution and can detect trace elements within the parts per billion range. The micro-rebound hammer was used to generate a reduced Young's modulus (E*), which gives a measure of the rock strength with negligible impact on the rock itself. In order to explore, visualize and understand the dataset generated, principal component analysis (PCA) was applied to emphasize variation and bring out strong patterns in the dataset. The first two dimensions of PCA express 57.09% of the total dataset inertia; that means that 57.09% total variability in the data is explained by the planes/dimensions. The first dimension, which showed a strong positive correlation to clay forming minerals and rock strength, was tentatively identified as the clay gradient. The second dimension describes diagenetic alteration processes responsible for the enrichment of elements such as Ni, Mo etc. Further, a positive correlation was established between E* and four elements Cobalt (Co), Strontium (Sr), Titanium (Ti), and Zircon (Zr). Remarkably, Silicon (Si) had a negative correlation with all elements but positive correlation with porosity and permeability. We therefore identified Co, Ti, Sr, and Zr as proxy for the determination of rock strength specific for studied samples and proposed a workflow based on our sequences of analysis and interpretation. Furthermore, we identified four chemo-mechanical facies through hierarchical clustering of the product of the PCA. This presented methodology could be specifically useful for geomechanical characterization of rocks; a key requirement needed for in-situ stresses estimation, wellbore stability analysis, reservoir stimulation and compaction, pore pressure prediction, and more importantly for characterizing drill cuttings where size and time are limiting. Drilling operations require constantly evolving cost effective and time efficient techniques, the proposed workflow will serve these purposes i.e. rapid determination of elemental composition (microxrf) coupled with E*will give a reliable proxy for rock strength. The technique can be applied to, drill cuttings, slabs and whole core directly without prior sample preparation.

Mineralogical, Elemental, and Spatial Variability of Volcaniclastics in Fluvio‐Coastal‐Aeolian Sedimentary Systems and Their Insights for Mineral Sorting on Mars

“…In order to reduce the relative effects of variables measured on different instruments, scales and ranges, the datasets we obtained from XRD and XRF were all scaled and centered; that is, by subtracting each variable's mean from its individual score and then dividing by the variable's standard deviation. This linear transformation does not alter the correlations among the variables, and it is often recommended (Hussain et al., 2018). We employed two multivariate statistical techniques to find patterns in our high dimensional data set and establish relationships.…”

Mineralogical, Elemental, and Spatial Variability of Volcaniclastics in Fluvio-Coastal-Aeolian Sedimentary Systems and Their Insights for Mineral Sorting on Mars

“…In order to reduce the relative effects of variables measured on different instruments, scales and ranges, the datasets we obtained from XRD and XRF were all scaled and centered; that is, by subtracting each variable's mean from its individual score and then dividing by the variable's standard deviation. This linear transformation does not alter the correlations among the variables, and it is often recommended (Hussain et al, 2018). We employed two multivariate statistical techniques to find patterns in our high dimensional dataset and establish relationships.…”

Impact of magmatic intrusion on diagenesis of shallow marine sandstones: An example from Qasim Formation, Northwest Saudi Arabia

“…Sandstone reservoirs are key exploration targets for hydrocarbons, geothermal energy, and CO 2 capture and storage (Al-Ramadan et al, 2005;Saner et al, 2006;Xia and Wilkinson, 2017;Hussain et al, 2018;Allen et al, 2020;Herlambang et al, 2022). Additionally, as the energy demand in the world is increasing, and hydrocarbon exploration moves towards deeper, morechallenging systems such as basins affected by igneous intrusion activity (Haile et al, 2019;Duffy et al, 2021;Rong et al, 2021), it is important to understand the interaction of diagenesis and magmatic intrusions on the reservoir quality of sandstones.…”