Fluid and Petrophysical Prediction in The Elastic
Domain Using Neural Network Method
Hermana, Maman; Najmi, Muhammad; Tuan Harith, Zuhar; Sum, Chow W.
Universiti Teknologi Petronas, Tronoh, Malaysia.
Interpretation in elastic
impedance
domain is a new method in geophysical prospect evaluation and development in Oil & Gas industry. Inspired by the success in lithology and fluid discrimination in
elastic
domain, we applied Neural Network technique into
elastic
volume to predict fluid and petrophysical properties in the reservoir. This study consist of three phases: 1) Correlation study between
Elastic
Impedance
log and Petrophysical Log to determine chi angle optimum, 2) AVO response and EEI projection performed to construct seismic equivalent to petrophysical properties. To convert the reflectivity of EEI into
impedance
domain, the colored inversion is applied. 3) The relative
impedance
of petrophysical volume evaluated using supervised ANN method to determined fluid and petrophysical properties. The result shows there is a good correlation between petrophysical log and EEI log: maximum coefficient correlation between porosity, water saturation and resistivity occur at 23, 25 and 32 degrees. Relative
impedance
domain which is obtained from colored inversion has been checked with well logs, there is good matching between seismic
elastic
impedance
with logs. Finally, the absolute porosity, water saturation and resistivity estimated using supervised neural network. The results is consistent with petrophysical logs.
AAPG Search and Discovery Article #90155©2012 AAPG International Conference & Exhibition, Singapore, 16-19 September 2012