Application of machine learning techniques in estimation of fracture porosity using fuzzy inference system for a FGB reservoir in Cuu Long basin, Vietnam
Abstract
Determination of porosity of a fractured granite basement (FGB) reservoir in the Cuu Long basin has always been a challenge for petrophysicists. In this study, an analysis of fracture porosity was successfully conducted, using a machine-learning technique, i.e., fuzzy inference system (FIS), the well log data including gamma ray (GR), deep resistivity (LLD), shallow resistivity (LLS), sonic (DT), bulk density (RHOB), neutron porosity (NPHI), photoelectric factor (PEF) and caliper (CAL) from two wells BHX01 and BHX02, were used as the input for FIS analyses. Fracture porosity calculated by conventional method was found between 0.01 and 2.24% for BHX01 and between 0.15 and 6.63% for BHX02, respectively. These values match very well with those predicted by various FIS techniques, i.e. Sugeno, Mamdani and Subtractive FIS models. It is expected that the approach of using FIS in petrophysical analysis as presented in this paper can be further applied for other fractured granite basement reservoirs in the Cuu Long and Nam Con Son basins, offshore southern Vietnam.
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