Geochemical Machine Learning in Sandstones: Predicting Porosity, Permeability and Facies from Handheld XRF Compositions

Handheld X-ray fluorescence (HHXRF) scanners generate rapid, low-cost geochemical datasets from core and cuttings, yet their potential for quantitative reservoir characterisation remains largely unrealised, partly because standard multivariate methods are inappropriate for the compositional nature of geochemical data. Here we test, for the first time within a compositional data analysis framework, whether centred log-ratio-transformed HHXRF element compositions can simultaneously predict plug-scale porosity, directional permeability and facies in a siliciclastic reservoir in a continuously cored Brent Group well from the Northern North Sea. The cored interval was logged for facies, sampled for routine core analysis, and analysed by HHXRF at plug sample positions. Sixteen consistently detectable elements were transformed using centred log-ratios to respect the compositional nature of the data, and four Random Forest models were trained: regression models for porosity, horizontal permeability and vertical permeability and a seven-category facies classifier. Models were evaluated using out-of-bag predictions, residual analyses, class-wise reliability metrics and permutation-based variable importance. The models reproduce porosity and permeability with high coefficients of determination (R2 > 0.95) and low errors relative to observed ranges and achieve facies classification with substantial agreement (κ = 0.705), with best performance in clean sandstone facies. Predictive skill is dominated by a consistent subset of elements (notably Ca, Ti, Si, V, Zn and Rb), linking bulk composition to mineralogy, depositional texture and diagenetic modification. These results demonstrate that compositional data from HHXRF alone can quantitatively recover key reservoir attributes and facies architecture at plug scale, establishing bulk geochemistry as a robust proxy for reservoir quality in quartz-rich, moderately buried siliciclastic reservoirs. The workflow provides a methodological template for integrating compositional geochemistry with machine learning in subsurface characterisation and, pending multi-well validation, offers a route to cost-effective prediction of porosity, permeability anisotropy and facies from cuttings or high-resolution core scanning. The workflow has direct application to geocellular model population in carbon and hydrogen storage sites, geothermal reservoirs and conventional hydrocarbon fields.