Seismic reprocessing, simultaneous inversion and interpretation, DTEK Oil and Gas, Ukraine

Solution

Chris Rudling, Principal Geophysicist at RPS, developed a quantitative interpretation (QI) project for DTEK to address this challenge. He shared the findings and results earlier this year at the 81st EAGE Conference & Exhibition.

The QI workflow we applied used a combination of statistical rock physics (to capture variability and uncertainty in rock properties) and high-quality seismic data obtained through a bespoke re-processing solution. This reduced the noise and improved event resolution and continuity.

Seismic inversion was used to generate rock property volumes, which were quantitatively compared to the rock physics model to produce probabilistic estimates of lithology. This in turn enabled direct reservoir and porosity prediction using statistical rock physics models and simultaneous inversion.

Challenge

The Semyrenky reservoirs in the Ukraine are very deep at over 5km, and drilling costs are as much as 10x higher than typical onshore land projects. Noisy and discontinuous seismic data meant that identification and mapping of sands was problematic, which led to several sub-optimal well placements. 

The high expense of drilling to these depths means that wells must yield good production rates for a sustained period to make them economical. Optimal production is typically associated with higher-porosity interconnected sands. Seismic reflectivity data alone cannot reliably identify these productive, high porosity sands.

RPS developed a geophysically robust model using existing seismic data to provide analysis to DTEK for making production optimisation decisions. The two wells drilled using this model, showed a good match between predicted results and well logs for both reservoir presence and quality.

The ability to correctly predict sand distribution and porosity over the field is therefore of great value in identifying sweet spots and will be used in further field development.

As a result, location decisions, and hence production levels, are now driven by greater insight into the distribution of sand and porosity.