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Fluid discrimination using bulk modulus and neural network
Hydrocarbon prediction using the rock physical parameters is a common technique in the oil and gas industry. However, the rock physical parameters are controlled by porosity, the volume of clay, pore-filled fluid type and lithology simultaneously. Many methods are proposed to predict the existence of hydrocarbon. This paper proposes a new method Î�K which is the difference between the real bulk modulus and the bulk modulus in the brine- substitute case. The algorithm is validated through stochastic numerical modelling. The brines are separated by the Î�K, and the gas can be detected with acceptable accuracy. Furthermore, a model using deep learning approach is trained to predict the Î�K. The trained model is effective that the predicted values using this model have a strong correlation with the original Î�K. The Î�K can be applied to the data which contains Vp, Vs and density using this approach model. In this study, the Î�K is applied to the Marmousi II dataset to examine the performance and yields a good result. The combination of the deep learning and the Î�K improves our ability in hydrocarbon prediction. © 2019, International Petroleum Technology Conference
International Petroleum Technology Conference (IPTC)
International Petroleum Technology Conference 2019, IPTC 2019
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