A globally optimized factor analysis is presented to estimate shale volume of
groundwater formations. Wireline log data, including natural gamma-ray intensity
(GR), spontaneous potential (SP), density (GG), neutron-neutron intensity (NN) and
shallow resistivity (RS) logs are processed simultaneously to estimate the values of
factor scores along a section of the investigated borehole. Then the so-called first
factor log is related to the shale volume of formations by regression analysis. In the
investigated borehole, a strong linear relationship is found between the first factor log
and the shale volume. Invasive weed optimization is implemented into factor analysis
to reduce the misfit between the measured and theoretical data calculated by the factor
model and it also permits the estimation of theoretical values of well logging data.
The method provides an independent estimate of shale volume, which can be later
used in the further interpretation of data.
Cím:
Invasive weed optimization driven factor analysis for lithological characterization of aquifers
A globally optimized factor analysis is presented to estimate shale volume of
groundwater formations. Wireline log data, including natural gamma-ray intensity
(GR), spontaneous potential (SP), density (GG), neutron-neutron intensity (NN) and
shallow resistivity (RS) logs are processed simultaneously to estimate the values of
factor scores along a section of the investigated borehole. Then the so-called first
factor log is related to the shale volume of formations by regression analysis. In the
investigated borehole, a strong linear relationship is found between the first factor log
and the shale volume. Invasive weed optimization is implemented into factor analysis
to reduce the misfit between the measured and theoretical data calculated by the factor
model and it also permits the estimation of theoretical values of well logging data.
The method provides an independent estimate of shale volume, which can be later
used in the further interpretation of data.