A Method to Differentiate Degree of Volcanic Reservoir Fracture Development Using Conventional Well Logging Data—An Application of Kernel Principal Component Analysis (KPCA) and Multifractal Detrended Fluctuation Analysis (MFDFA)

Fracture is the main pore space for volcanic reservoir, serving as the controlling factor of reservoir productivity. Conventional well logging data often fail to fracture characterization and classification in volcanic reservoir since the degree or extent of the fracture development varies in scales in different locations. A method for fracture developing degree discrimination, based on a combinational algorithm of kernel principal component analysis (KPCA) and multifractal detrended fluctuation analysis (KPCA-MFDFA), is proposed. The first kernel principal component (KPC_1), mostly characterizing the reservoir property, is extracted from conventional well logging data. Multifractal parameters, such as multifractal dimension, mass exponent, multifractal spectrum, and singularity strength, are calculated by MFDFA. A cross-plot between the maximum multifractal dimension difference and range of singularity strength is established to investigate the relationships between multifractal parameters and fracture developing degree.