Fluid discrimination is quite important for offshore oil exploration which always means high risks and investments. The goal of the study is to predict the fluid content of the reservoirs in the B oilfield of Bohai Sea. As a normal heavy oil field, precise fluid discrimination is a challenging task. In order to overcome the challenge, a modified Poisson impedance (PI) attribute named Fluid impedance (FI) and a consequent multi-attribute inversion method are adopted. The Fluid impedance (FI), which is based on Poisson impedance, is more sensitive to the fluid content since it is calculated through the correlation analysis between PI curves of different rotation angles and water saturation logs. Then the multi-attribute inversion method based on Probabilistic Neural Network (PNN) is applied to get the water saturation volume for fluid discrimination. What’s more, the use of Fluid impedance, along with other hydrocarbon related seismic attributes has resulted in a much improved prediction of water saturation volume with high resolution and accuracy. The application demonstrates that the proposed method can improve the reliability of fluid discrimination significantly.
Presentation Date: Wednesday, October 19, 2016
Start Time: 8:50:00 AM
Presentation Type: ORAL