Field Applications of Waterflood Optimization via Optimal Rate Control With Smart Wells

Abstract

Summary Waterflood optimization by means of rate control is receiving considerable attention because of increasing deployments of smart well completions and i-field technology. The use of inflow control valves (ICVs) 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 (Alhuthali et al. 2007, 2008), which relies on equalizing arrival time of the waterfront at all producers to maximize 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 by means of 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 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 that has more than 50 years of historical oil production. The optimization is performed for 20 years on a portion of this field that 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.

Semi-analytical modeling of advanced water injection in low permeability reservoirs under non-Darcy-flow condition

“…Specially, taking equation (11) integral in the direction of well, the Laplace solution of a vertical well is given by…”

“…[7][8][9] Because of the higher seepage resistance and non-Darcy flow, the sweep efficiency of water flooding is low. 10,11 The advanced water injection method is an effective technology to improve the sweep efficiency and oil recovery. The water is injected into the formation before the producers begin to operate, and a new equilibrium state of higher formation pressure is formed.…”

Application of simulated annealing optimization algorithm to optimal operation of intelligent well completions in an offshore oil reservoir

“…They found that closedloop feedback control yields positive gains in NPV for the majority of reservoir behaviors investigated, and higher gains than the open-loop strategy since closed-loop control can also yield positive gains in NPV even when the reservoir does not behave as expected. Datta-Gupta et al (2010) proposed an approach that is computationally efficient and suitable for large field scale. They used streamlines to analytically compute the sensitivity of the arrival times with respect to well rates.…”

Results of the First Norne Field Case on History Matching and Recovery Optimization Using Production and 4D Seismic Data

“…The variable ' i t is the calculated front arrival time (or time of flight) at production well i, and the desired arrival time t d is given by the arithmetic average of ' i t over all Np wells during each optimization iteration (Alhuthali et al 2010). The vector q contains the control variables and has a dimension of Np.…”