Improved recognition of rock formation on the basis of well logging and laboratory experiments results using factor analysis

Abstract

Several data sets from the Silurian and Ordovician formations from three wells on the shore of Baltic Basin in Northern Poland prepared on the basis of well logging data and results of their comprehensive interpretation were used in factor analysis. The goal of statistical analysis was structure recognition of data and proper selection of parameters to limit the number of variables in study. The top priority of research was recognition of specific features of claystone/mudstone formations predisposing them to be potential shale gas deposits. The identified data scheme based on data from one well, was then applied to: 1) well 2 and well 3 separately, 2) combined data from three wells, 3) depth intervals treated as sweet spots, i.e., formations of high hydrocarbon potential. Numbers of samples from well logging were proportional to number of laboratory data from individual formations. The extended data set comprising all available log samples in explored formations was also prepared. Outcomes from standard (Triple Combo—natural gamma log, resistivity log, neutron log and bulk density log and Quad Combo—with addition of sonic log and spectral gamma log) and sophisticated (GEM™—Elemental Analysis Tool, Wave Sonic and Nuclear Magnetic Resonance—NMR) logs were the basis for data sets. Finally, laboratory data set of huge amount of variables from elemental, mineralogical, geochemical and petrophysical laboratory experiments was built and verified in FA to select the most informative components. Conclusions on the data set size, number of factors and type of variables were drawn.