Application of Pseudo Voidage Replacement Ratio Pseudo VRR Concept to Optimize 5 Spot Polymer Flood: A Mangala Field Case Study

Grover, Kavish (Cairn Oil & Gas, Vedanta Limited) | Kolay, Jayabrata (Cairn Oil & Gas, Vedanta Limited) | Kumar, Ritesh (Cairn Oil & Gas, Vedanta Limited) | Ghosh, Priyam (Cairn Oil & Gas, Vedanta Limited) | Shekhar, Sunit (Cairn Oil & Gas, Vedanta Limited) | Agrawal, Nitesh (Cairn Oil & Gas, Vedanta Limited) | Das, Joyjit (Cairn Oil & Gas, Vedanta Limited)

OnePetro 

Abstract

For any typical water flood or polymer flood management, maintaining optimum Voidage Replacement Ratio (VRR) is most crucial for optimizing reservoir performance. In a typical patternflood, a single injector supports many nearby producers, determining its contribution to particular producer is subjective and has inherent uncertainties. To avoid these uncertainties in allocation factor, a novel approach using simulation model based voidage compensation on pattern by pattern basis has been proposed in this paper.

History matched simulation model, which has been sectored into 5-spot producer centric patterns, forms the basis of this study. Voidage replacements are analyzed on these producer centric 5-spot patterns. Sectoral voidage created is determined using change in hydrocarbon pore volume (HCPV), water pore volume (WPV) and production from the sector. Sectoral Voidage Compensation Ratio (or Pseudo VRR) thus calculated is representative of the net change due to injection and production. The advantage is that it does not require any numerical allocation factor, rather is based on fluid movements within a pattern as predicted by the simulation model. This method thus provides a new approach to analyze pattern performance.

Along with VRR, pattern wise recovery and interwell channeling/cycling are the key parameters for any water flood performance analysis. A workflow has been proposed to rank the patterns based on these parameters and categorizing them into problem buckets. Actions corresponding to each bucket have been proposed. This forms the basis of strategizing improvements in well-by-well and pattern-by-pattern performance for optimizing field performance.