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Fast elemental analysis in the wellsite is crucial across the energy sector, where timely and accurate geological information drives operational efficiency and safety. In geothermal projects, rapid geochemical characterization aids in identifying reservoir quality and alteration zones, optimizing drilling locations, and reducing nonproductive time. For carbon storage, a quick assessment of rock mineralogy ensures suitable cap rock integrity, which is essential for environmental safety. In hydrocarbon exploration and production, immediate elemental data enable real-time lithology evaluation, improving well placement, reducing drilling risks, and lowering operational costs. Overall, integrating advanced machine learning with portable, on-site Laser-Induced Breakdown Spectroscopy (LIBS) technology provides fast, reliable elemental data that support adaptive and cost-effective resource development in these critical operations. In retrospect, this paper explores and evaluates the application and coupling of the Bayesian optimization process for hyperparameter tuning with support vector machine, with the objective of quantifying major oxide elements present in rock cuttings samples from LIBS. The main objective of using this process is to automatically optimize the model performance while minimizing the manual iterative and random trial and testing of the hyperparameters. In this investigation, over 1000 samples were prepared to develop the predictive model, where X-ray fluorescence was used as the reference method for obtaining concentration. The model performance was evaluated using multiple metrics, achieving an R-squared value in the range of 0.93 to 0.97, indicating the model’s reliability and accuracy when dealing with high-dimensionality and nonlinearity that are exhibited in the data set.
Alsaud et al. (Wed,) studied this question.