Summary Normally only approximately 30% of the oil in a reservoir is extracted during primary production, but using secondary-production methods such as water or gas injection, it is often possible to increase that percentage significantly and maintain the production rate of a reservoir over a longer period of time. In reservoirs under water or gas injection, additional gains can be obtained through an efficient strategy for management of front movement and reservoir sweep. The objective of reservoir production optimization is to maximize an outcome such as sweep efficiency or net present value (NPV) through the control of completion rates or pressures. Using optimization methods, it is possible to compute control settings that result in increased oil production and decreased water production compared with production from standard practices. In this paper, we focus on optimization using sequential quadratic programming (SQP) with an ensemble-based approach to estimate the gradient for the optimization. Although uncertainty in reservoir properties is usually important for the computation of optimal controls, here we use a single realization of the reservoir to evaluate the efficiency of the optimization algorithm. The most expensive aspect of gradient-based optimization is usually the computation of gradients. Most practical production-optimization problems involve large-scale, highly complex reservoir models with thousands of constraints, which makes numerical calculation of the gradient time consuming. Here, we use an ensemble-based approach for finding gradients and use localization to improve estimation of the gradient from a small number of realizations. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used for maximizing the objective function, with the Hessian estimated from a sequence of estimates of the gradient. Improving the gradient approximation using localization results in improvement to the Hessian approximation. A second important aspect of the efficiency of the method is the identification of active constraints. In this paper, we use a method for eliminating nonnegativity constraints to decrease computation time and an updating procedure to solve each iteration of SQP much faster than the base case. Both the speed of the algorithm and the final NPV were increased significantly. We evaluate the method by applying it to optimization of control settings in the Brugge field. Brugge is a 3D synthetic model designed by TNO with 20 vertical producers and 10 vertical peripheral water injectors. All of the producers and injectors are smart wells whose downhole chokes must be adjusted to optimize NPV. The total number of completion flow rates to be controlled is 84 at each timestep, with 40 timesteps (every 6 months). There are 1,200 inequality constraints on total well liquid rates and 3,360 nonnegativity constraints on completion liquid rates. There are also inequality constraints on the bottomhole pressure (BHP) for wells at each time period.

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In development phase of a reservoir and drilling the production wells, it is necessary to drill these wells in appropriate points in order to have no interference in their drainage areas, or have minimum value of interference. In this case, we can produce oil/gas in high flow rate; And in the presence of favorite conditions, recovery factor of the reservoir can be increased. The objective of this work is to develop a methodology for one of Iranian oil fields by combining the utilization of two important tools: traditional simulation (using of different property maps) and streamline simulation. The conventional simulation (based on finite difference) is used to do the main reservoir simulation by compositional modeling. All the economic analysis is made based on conventional simulations output. Computation of different property maps by this simulator are used for investigation of oil volume, permeability, porosity, reservoir thickness and finally oil saturation maps of our reservoir. The streamline simulation is a supporting tool employed to give more reliability and to bring up insights about the optimization process and speedup to the process, mainly in the identification of best locations of news wells and drainage volume. Also streamline simulation is used to study the fluid flow pattern in the field and determining the efficiency of infill wells. In this work we use threshold for different property maps of our reservoir in order to find best location of infill wells, then streamline simulation help us to confirm and optimize these locations based on streamline tracing and drainage volume calculations. We defined several scenarios in order to maximize oil production of our reservoir. Results show that by using streamline technique we can improve recovery of reservoir respect to conventional methods. Introduction In December 2003 the investigated field celebrated 39 years of production. The field contains undersaturated oil and is being developed under aquifer drive and has 45 exploration wells. Modern streamline-based reservoir simulators are able to account for actual field conditions such as 3D multiphase flow effects, reservoir heterogeneity, gravity, and changing well conditions. Streamlines provide new flow information (i.e., well connectivity, drainage volumes, and well allocation factors) that cannot be derived from conventional simulation methods (Pallister and Ponting 2000). One of the main advantages of streamline simulation is its ability to display paths of fluid flow. The streamline simulation results substantially have more value as a reservoir management tool when used in conjunction with traditional reservoir engineering techniques such as standard finite difference simulators (Samier and Thiele 2001). There are different ways to calculate (or estimate) drainage area and drainage radius. Well test analysis, use of dimensionless parameters, material balance and volumetric method, Pressure mapping (for streamline tracing) and use of decline curves matching (although it seems not useful in the early of development of the field). In this paper we use streamline simulation for estimating well drainage volume in a multiple well reservoir. By using of this method we can find best location for drilling of new wells and investigate effect of drainage volume of adjacent wells on each others (Hurst 1987). This technique is based on calculating reservoir pressure throughout the field and producing pressure maps over that field. From the pressure mapping, streamlines tracing the path of fluid towards the well can be plotted and drainage areas discerned (Anderson 1991).

Normally only about 30% of the oil in a reservoir can be extracted, but using secondary production methods such as water or gas injection it is often possible to increase that percentage significantly and maintain the production rate of a reservoir over a longer period of time. In reservoirs under water or gas injection additional gains can be obtained through an efficient strategy management of front movement and reservoir sweep. The objective of reservoir production optimization is to maximize an outcome such as sweep efficiency or net present value (NPV) through the control of completion rates or pressures. Using optimization methods, it is possible to compute control settings that result in increased oil production and decreased water production compared to production from standard practices. In this paper, we focus on optimization using SQP with an ensemble-based approach to estimate the gradient for the optimization. Although uncertainty in reservoir properties is usually important for the computation of optimal controls, here we use a single realization of the reservoir to evaluate the efficiency of the optimization algorithm. The most expensive aspect of gradient-based optimization is usually the computation of gradients. Most practical production optimization problems involve large-scale, highly complex reservoir models with thousands of constraints, which makes numerical calculation of the gradient time consuming. Here, we use an ensemble-based approach for finding gradients and use localization to improve estimation of the gradient from a small number of realizations. The BFGS method is used for maximizing the objective function, with the Hessian estimated from a sequence of estimates of the gradient. Improving the gradient approximation using localization results in improvement to the Hessian approximation. A second important aspect of the efficiency of the method is the identification of active constraints. In this paper, we use a method for eliminating non-negativity constraints to decrease computation time and an updating procedure to solve each iteration of SQP much faster than the base case. Both the speed of the algorithm and the final NPV were increased significantly. We evaluate the method by applying it to optimization of control settings in the Brugge field. Brugge is a 3D synthetic model designed by TNO with 20 vertical producers and 10 vertical peripheral water injectors. All of the producers and injectors are smart wells whose downhole chokes must be adjusted to optimize NPV. The total number of completion flow rates to be controlled is 84 at each time step, with 40 time steps (every six months). There are 1200 inequality constraints on total well liquid rates and 3360 non-negativity constraints on completion liquid rates. There are also inequality constraints on the bottom-hole pressure for wells at each time period.