Functional Network softsensor for formation porosity and water saturation in oil wells

Formation porosity and water saturation play important role in evaluating potential oil reservoirs and for drafting development plans for new oil fields. This paper presents a novel method for estimating these two important parameters directly from conventional well measurements. The recently proposed Functional Networks technique is applied for rapid and accurate prediction of these parameters, using six and five basic well log measurements as data for estimating porosity and water saturation respectively. Functional network is a generalization of the conventional Feed Forward Neural Networks, which overcome many of the drawbacks of the conventional neural network techniques. The proposed functional network was trained using data gathered from two wells in the Middle East region. Results obtained from this case study using the proposed intelligent technique have shown to be fast and accurate.