Abstract

Minas field, operated by PT Caltex Pacific Indonesia in Central Sumatra, is characterized by a complex fluvio-deltaic depositional system where lateral and vertical distribution of sand bodies is very heterogeneous. The existing high-resolution sequence stratigraphic framework based on conventional cores and well logs is inadequate for accurately mapping reservoir-scale flow units. To overcome this problem, seismic multi-attribute analysis utilizing high-resolution 3D seismic data has been investigated. The prime objective is to differentiate sands from shales using the seismic data, especially for inter-well correlations (injector to producer). The integrated attributes include: 1) internal seismic attributes (amplitude, frequency, etc), and 2) log property attributes (porosity, gamma ray, etc) derived from cross-plots between the internal seismic attributes and well data.

Cross-plot analyses were generated for sand/shale intervals that roughly correspond to five main oil-producing reservoirs. These five intervals are defined and bracketed by key field-wide sequence stratigraphic marker horizons: 1) M1 - SB1, 2) SB1 – SB2, 3) SB2 – M6, 4) M6 – SB4, and 5) SB2 – SB2.1. For Interval 1, the acoustic impedance (AI) can distinguish sands from shales, thus seismic inversion is recommended for this interval. Intervals 2 to 5 have overlapping AI and it is very difficult to differentiate sand from shales using the normal amplitude sections.

Therefore, the most applicable internal seismic attributes are the derivatives of frequency. These are used to generate seismic pseudo-gamma ray, pseudo-porosity and pseudo-density sections, which are derived from the multi-attributes analysis. Each of these has proven to be effective in differentiating sand and shale in different portions of Intervals 2 to 5. Consequently, the system-tracts can also be delineated more easily, thus providing an effective tool for guiding well correlations.

The results of combining various derived attributes provide additional support for the detailed correlation and mapping of the location, geometry and quality of individual sand bodies within Minas field. This will lead to improved waterflood optimization and infill well selection. Newly acquired well data have confirmed the results obtained in this study.