Resistivity Reconstitution Multi-attributes Inversion and the Application in Carbonate Reservoir Prediction

It is well known that carbonate reservoir prediction is the worldwide problem due to it’s strong heterogeneity. The paper propose a novel carbonate reservoir prediction technology, which firstly constructs a new resistivity log based on the difference between deep and shallow lateral resistivity logs to reflect reservoir properties change. Then resistivity reconstitution multi-attributes inversion is conducted, and the algorithm implementation includes three steps: (1)Utilizing impedance inversion to characterize the reservoir distribution features; (2) And then combining impedance with the other reservoir properties related seismic attributes, we take the reconstitution reflectivity logs as objective function to construct multi-variables linear inversion, and use stepwise regression algorithm to sort the reservoir properties sensibility of seismic attributes; (3) Finally using probabilistic neural network algorithm to make multi-attributes resistivity reconstitution inversion for the choosing seismic attributes. The application of proposed method shows the good performance on predicting the good quality reservoir distribution, which is beneficial for the further oilfield updating and production performance managing.