XRF-FTIR Data Fusion and Machine Learning Models for Mineralogical Analysis of Solid Mineral Mixtures and Solid Dispersions in Drilling Fluids
Authors: Atheaux I. , Hughes T. L. , Peng J. , Johnson C. E. , Yarman C. E. , Medvedev A. , Makarychev-Mikhailov S. 
Published: February 2025

The analysis of solid contents in drilling muds is a crucial part of the API mud testing protocol, specifically outlined in API Recommended Practice 13B-2, with standardized procedures for testing oil-based drilling fluids. These tests help maintain the integrity and performance of drilling operations. The manual retort test, although labor-intensive, is performed for detecting high-gravity solids (HGS) and low-gravity solids (LGS) in the drilling mud. In the vision of moving towards online monitoring of solids contents in drilling muds, enhanced with mineralogical analysis, promising approaches and technologies such as combining measurements from different analytical techniques with machine learning models for real-time data integration have been explored. Particularly, the estimation of reactive clays content in drilling fluids is desirable to prevent challenging issues, including wellbore stability and stuck pipe.

Mineralogical analysis of solids in mineral mixtures and dispersions in non-aqueous drilling fluids (NAF) is performed with two analytical techniques, X-ray fluorescence (XRF) spectrometry and Fourier transform infrared (FTIR) spectroscopy. Novel data fusion and machine learning methodologies are developed, which rely on the combination of XRF and FTIR spectra. This approach allows quantification of the concentration of minerals, mineral groups and chemical elements with remarkable accuracy, not achievable by using XRF or FTIR data alone.

The two analytical techniques, XRF and FTIR, are complementary as they provide respective information regarding the concentration of elements and chemical bonds. Both techniques are easy to use (with minimal sample preparation) and they are rapid and affordable which means that they are excellent candidates for field application. Experimental procedures for lab sample formulation with up to ten different minerals and measurement protocols are described. Spectra preprocessing, a critical step in modeling, is further explained. Finally, several machine learning algorithms are evaluated on individual XRF or FTIR spectra and on XRF-FTIR data fusion based datasets.

The methodology disclosed in our recent paper (Atheaux et al. 2024) is significantly extended and generalized here using larger datasets pertaining to pure mineral mixtures and solid mineral dispersions in NAF. New data fusion techniques are developed and discussed which enable more reliable mineral and element analysis in complex multicomponent and multiphase systems. Machine learning models based on different dataset combinations are validated on an independent test set. Mineral analysis errors of prediction (root mean square error, RMSE) for all minerals are less than 2.0 wt% in solid mixtures and less than 0.65 wt% in drilling fluid samples. Chemical elemental analyses, traditionally done by XRF alone, are further shown to be significantly improved when FTIR spectra are used in combination with XRF data. Finally, drilling physical properties, like density, can be also quantified with great accuracy.

The application of two fast and relatively simple measurements for drilling fluids provides quantification of not just total LGS and HGS contents (as given by the traditional retort analysis), but also LGS solids mineralogy, very valuable for understanding composition of the formation being drilled and effects of LGS content and composition on drilling fluid properties. The developed methodology is believed to be, at least, complementary, and potentially even competitive to current mud logging cuttings analysis methods.