Fluid optical database reconstruction methods and applications thereof

Abstract

Mutual-complementary modeling and testing methods are disclosed that can enable validated mapping from external oil and gas information sources to existing fluid optical databases through the use of forward and inverse neural networks. The forward neural networks use fluid compositional inputs to produce fluid principal spectroscopy components (PSC). The inverse neural networks apply PSC inputs to estimate fluid compositional outputs. The fluid compositional data from external sources can be tested through forward models first. The produced PSC outputs are then entered as inputs to inverse models to generate fluid compositional data. The degree of matching between reconstructed fluid compositions and the original testing data suggests which part of the new data can be integrated directly into the existing database as validated mapping. The applications of using PSC inputs to reconstruct infrared spectra and estimate oil-based-mud (OBM) contamination with endmember spectral fingerprints are also included.