The reservoir management processes of heavy oil reservoirs are challenging and require different types of considerations when contrasted to conventional reservoirs. The development and optimization of heavy oil thermal EOR projects entails the injection of steam through dedicated injectors to assure the viscosity reduction necessary for the oil to freely flow thorough the porous media into the producing wells. The four stages of a steamflood development project consist of ramp-up, peak and plateau, decline and tail-end production. Optimization of each stage is critical for the success of the project.
The current study addresses the tail-end stage of the steamflood project and focuses on the optimization component of steam used, particularly steam reduction, within reservoir management practices. The novel approach presented in this work allows successful identification of mature areas within the reservoir and/or projects that are ready and would benefit from significant steam reduction. The methodology uses heat management knowledge, large datasets, and artificial intelligence (AI) technologies. The workflow exploits the log data, existing full-field earth model, the current steamflood conditions, and automates the screening and ranking of maturity of development patterns in large heavy oilfields.
The methodology outperformed the previous manual approach, not only by better identifying areas and opportunities, but also optimizing the steam reduction schedule, and in certain cases recommending steam redeployment to potentially increase field production.