Field Applications of Waterflood Optimization via Optimal Rate Control With Smart Wells
- Ahmed Humaid H. Alhuthali (Saudi Aramco) | Akhil Datta-Gupta (Texas A&M U.) | Bevan Bun Wo Yuen (Saudi Aramco) | Jerry Pasco Fontanilla (Saudi Aramco)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Simulation Symposium, 2-4 February, The Woodlands, Texas
- Publication Date
- Document Type
- Conference Paper
- 2009. Society of Petroleum Engineers
- 2 Well Completion, 4.1.5 Processing Equipment, 4.1.2 Separation and Treating, 5.5 Reservoir Simulation, 1.6.9 Coring, Fishing, 5.5.7 Streamline Simulation, 5.5.8 History Matching, 1.6.6 Directional Drilling, 6.5.2 Water use, produced water discharge and disposal, 3.2.6 Produced Water Management, 5.4.1 Waterflooding, 7.6.2 Data Integration, 2.3 Completion Monitoring Systems/Intelligent Wells, 5.7.2 Recovery Factors, 5.2 Reservoir Fluid Dynamics, 5.1.2 Faults and Fracture Characterisation, 4.3.4 Scale
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Waterflood optimization via rate control is receiving considerable attention because of increasing deployments of smart well completions and I-Field technology. The use of inflow control valves (ICV) allows us to optimize the production/injection rates of various segments along the wellbore, thereby maximizing sweep efficiency and delaying water breakthrough. Field scale rate optimization problems, however, involve highly complex reservoir models, production and facility constraints, and a large number of unknowns. In this paper we propose an approach that is computationally efficient and suitable for large field cases. It is based on our previous work that relies on equalizing arrival time of the waterfront at all producers for maximizing the sweep efficiency. We use streamlines to efficiently and analytically compute the sensitivity of the arrival times with respect to well rates. We also account for geologic uncertainty via a stochastic optimization framework using multiple realizations. Analytical forms for gradients and Hessian of the objective functions are derived, making our optimization computationally efficient for large-scale applications. Finally, optimization is performed under operational and facility constraints using a sequential quadratic programming approach.
We demonstrate our approach using two field-scale examples. The first one is a synthetic example called "Brugge?? field, a benchmark case based on a North Sea Brent-type field. The production optimization of this field is carried out as part of a closed loop process where the production history is matched prior to the production optimization. The production optimization is performed over multiple realizations for 20 years and involves 30 wells equipped with three ICVs per well. The second example is a super-giant Middle Eastern field which has over 50 years of historical oil production. The optimization is performed for 20 years on a portion of this field which contains nearly 300 wells consisting of conventional vertical and horizontal wells and smart horizontal wells. In both examples, multiple field-related constraints are imposed, such as the maximum well injection and production rates, the maximum allowable drawdown, restriction on high water cut wells and voidage replacement for pressure maintenance. The results clearly demonstrate the viability of our approach, and the benefits of optimal rate control with a considerable increase in cumulative oil production and a substantial decrease in the associated water production.
It is well recognized that heterogeneity plays a major role in any waterflood project and can adversely impact sweep efficiency and oil production. Many authors have reported the use of production optimization as a means to counteract the impact of heterogeneity and improve sweep efficiency (Sudaryanto and Yortsos 2001; Brouwer and Jasen 2004; Alhuthali et al., 2007). A major focus of these studies has been to utilize the smart well technology to control the rate along the wellbore section.
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