Abstract
Q2-Sand of MC486, Gulf Coast is a mature low yield, condensate reservoir. The two issues to be resolved were 1) in-fill drilling requirements to optimize the production against early watering out of the existing wells, and 2) compressor schedules and capacities once the pressure is lower than the platform requirements. A new numerical flow model based on a geological, log, seismic, and production data was needed to resolve the issues. A multi-disciplinary characterization study to formulate final re-development and production optimization schemes is undertaken.
The static reservoir model was built from the 3-D seismic interpretations for the upper and the lower horizons. Thickness variations across the field and by horizons were estimated from thickness-amplitude correlations. Saturation, porosity, and net-to-gross ratio values were obtained from the core and log data. Variable directional permeabilities were calculated by a flow based upscaling technique using net-to-gross ratios and clean-sand and shale permeabilities. The compositional characteristics of the production were simulated with a fully compositional flow model.
The G&G model delineated Northern Prospect and the Main Reservoir. Both of these features were included into the numerical flow model along with the partially active aquifer that separates them. The numerical model was initialized and calibrated to the historical production data at the six wells. The current average pressure of the Northern Prospect is obtained. An additional aquifer support for the upper and lower sands was resolved, and the cross-flow spots between them were identified.
The integrated study was critical for optimizing recovery from this field. The optimum development plan recommends adding compressors and making re-completions to the upper sand and anticipates the recovery of 37 BCF more gas than previously predicted. Upper sand re-completions would drastically reduce water production and its handling costs as verified by a very recent re-completion job at an existing well.
