Estimation of Matrix Properties From Geochemical Spectroscopy Logs Using Machine Learning

General machine learning-based algorithms have been developed to predict important matrix properties and an estimate of their uncertainties. Grain density, neutron porosities, cross sections for thermal neutron absorption (Sigma) and fast neutron elastic scattering (FNXS) are the considered properties. The models are trained in a proprietary core database of more than 2000 samples from a global distribution of sedimentary rocks. The performance of the matrix property predictions is demonstrated in independent core data and case studies from the field. The machine-learning models provide a significant improvement in accuracy over linear regression models, while eliminating the need for user inputs. The models help to account for the presence of unmeasured thermal absorbers (e.g., boron) through geochemical associations among the available log elements. The nonlinear models also provide better precision despite accounting for statistical noise on the input elements from logging measurements. We use field datasets to illustrate the improved performance in wells from the North Sea penetrating complex lithologies.