Determination of Turbidite Lobe Distribution and Geometry in Middle Baong Sand



Middle Baong Sand (MBS) is the most prolific reservoir in North Sumatra Basin, yet to characterize this particular reservoir is quite a challenge when using conventional methods to define its facies, geometry, and distribution. Deposited as a submarine sand, lateral discontinuity of MBS is one of the problems that freeze the exploration and development strategies in the area. This study tries to re-determine the MBS by applying the Artificial Neural Network (ANN) approach in seismic multi-attribute analysis. The ANN approach in reservoir characterization and geometry analysis in MBS sand was conducted on seismic attributes data. One exploration well, SUT-1, was used as reference well to supervise the training. A complete data set provided from the well, support the electric-facies analysis that resulted in four different type of sand that developed in SUT-1, which are the coarse sand to silt from base to the top. Then this facies definition is applied to the multi-attribute analysis from 3D seismic data to predict the development and lateral facies distribution throughout the study area. Seismic analysis and interpretation showed that all of the facies are recognizable, and the best 6 (six) seismic attributes for each facies was selected to define facies’s geometry and mapped the lateral distribution. Combination and cross-plot were conducted to determine the best seismic attributes that is unique to differentiate one facies from the others, with the help of Artificial Neural Network. This approach and its result, provide new stratigraphic prospects effectively generated with significant amount of potential recourses, after years of no exploration activities in this field.

KEYWORDS: Turbidite, 3D Seismic, Multi-atribute Seismic, Artificial Neural Network.


Authors : J. Panguriseng, E. Nurjadi, W. S. Sadirsan, B. W. H. Adibrata, D. Priambodo