Neutron logs are routinely expressed as apparent neutron porosity based on the assumption of a freshwater-saturated homogeneous formation with solid composition equal to either sandstone, limestone, or dolomite. Rock formations are often extremely heterogeneous and consist of different minerals and fluids in varying proportions, which cause simultaneous matrix and fluid effects on neutron logs. Detailed quantification of formation mineral composition enables the correction of matrix effects on measured neutron logs to unmask fluid effects; this in turn enables accurate quantification of porosity and water saturation. Neutron-induced gamma-ray spectroscopy is one of the most direct means available to quantify in situ formation mineralogy but available spectroscopy-based interpretation methods are usually tool dependent and incorporate empirical correlations. We have developed a new interpretation method to quantify mineral concentrations through the joint nonlinear matrix inversion of measured spectroscopy elemental weight concentrations and matrix-sensitive logs, such as gamma ray, matrix photoelectric factor, matrix sigma (neutron capture cross section), and matrix density. The estimated mineralogy was used in the correction of matrix effects on porosity logs and subsequent calculation of true formation porosity. The water saturation was quantified through joint petrophysical interpretation of matrix-corrected porosities and resistivity measurements using an appropriate saturation model. The developed inversion-based interpretation method is applicable to a wide range of formation lithologies, well trajectories, and borehole environments (including open and cased hole environments), and it is independent of tool and neutron source type. Verification results with synthetic and field cases confirm that the spectroscopy-based algorithm is reliable and accurate in the quantification of mineral concentrations, matrix properties, porosity, and hydrocarbon saturation.
MET overexpression assessed by new interpretation method predicts gene amplification and poor survival in advanced gastric carcinomas