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A neuro-fuzzy based oil/gas producibility estimation method

We present a hybrid neuro-fuzzy technique for predicting producibility of a well. First, multilayer neural networks are used to compute petrophysical parameters such as quality control curves and permeability. In particular, neural networks are used to predict the permeability from nuclear magnetic resonance (NMR) logs. Next, the permeability is used as one of the input to a fuzzy logic inference engine that determines producibility and suggests a rank of production for multiple zones in a well. This technique is tested with well logs and results are comparable to expert identification of producible zones. The main advantages of the proposed model are faster processing time and less expert dependency during application.