Video: SAGD Production Optimization Through Preferential Steam Allocation
- Raphael Aboorvanathan (ConocoPhillips) | Amir Hossini (ConocoPhillips) | Chao Dong (ConocoPhillips) | Vahid Dehdari (ConocoPhillips) | Jay Feeney (ConocoPhillips)
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- Society of Petroleum Engineers
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- Document Type
- 2019. Copyright is retained by the author. This presentation is distributed by SPE with the permission of the author. Contact the author for permission to use material from this video.
- Allocation Steam Oil Ratio, SAGD Optimization Forecasting Planning Steam
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Optimizing steam-assisted gravity drainage (SAGD) performance in oil sands reservoirs relies on the quality of steam allocation decisions made across the well inventory. With finite facility steam generation capacity, SAGD producers are typically challenged with identifying the true opportunity cost of allocating steam volumes based on well performance. This paper presents a novel technique to inform steam allocation decisions and managing SAGD reservoir pressures in service of optimizing production and consequently improving the economic performance of the asset through smarter SAGD field development planning.
The concept of marginal steam-oil-ratio (mSOR) is introduced as a method of guiding steam allocation decisions. Marginal SOR is defined as the cold-water equivalent volume of steam required to produce the next marginal barrel of bitumen from the production system in a steam constrained environment. The metric represents the opportunity cost of deploying a barrel of steam to the next best alternative in steam allocation decisions. Dynamic quantification of mSOR over the plausible range of operating pressures for each producing entity (PRDE) in the inventory (such as a well group or drainage area) is critical to optimally allocating steam when faced with reservoir challenges such as reservoir complexity and heterogeneity and transient reservoir behaviors such as thief zone interaction.
This paper prescribes methodologies to analytically and empirically quantify mSOR for a SAGD production system. Additionally, application of the concept if field production optimization is discussed under the context of integrated production modeling and constrained flow network optimization problems. A case example of applying mSOR to guide steam allocation decisions at ConocoPhillips' Surmont SAGD asset is presented under a steam constrained environment. The mSOR guided solution is validated using brute-force enumeration of steam allocation outcomes in the production system to prove production optimality. The results from this dynamic steam allocation strategy guided by mSOR characterization show significant improvements in field oil rates, field steam management efficiency and consequently the economic value of the SAGD asset.