Abstract For oil reservoirs under water and/or gas drive, it is challenging and rewarding to effectively manage the water/gas fronts to maximize the sweep efficiency. In the past, a wide variety of approaches were developed and implemented for optimizing the reservoir performance by smartly adjusting operational controls. With recent developments in monitoring and control, the opportunity to optimize reservoir production through frequent adjustment of well controls has substantially increased. In this paper, we propose an improved ensemble-based approach for production optimization using Conjugate Gradient method (CGEnOpt). The proposed method results in faster convergence rate as a result of using the conjugate gradient directions instead of steepest ascent search directions. The net present value (NPV) of a single reservoir is optimized by obtaining the gradient of the NPV from an ensemble of control variables.
The effectiveness of the proposed method is demonstrated on a 3-D synthetic reservoir problem with 8 producers and 3 injectors. The production and injection rates are used as control variables to maximize NPV of the reservoir.
The results indicate that the control settings from the proposed method result in a significantly higher NPV of the reservoir as compared to the reference case. The number of iterations needed for the proposed method is significantly reduced compared to the standard ensemble-based optimization. The proposed method is able to allocate the production and injection rates effectively which results in a substantial decrease in the amount of water produced.
The proposed method is completely adjoint-free and is independent of the reservoir simulator used for the prediction simulation run which makes it fairly robust. Thus, it can be used with any existing simulator with minimal code development. The CGEnOpt technique is innovative for not requiring explicit computation of the gradient which makes it easy to implement.
Introduction Brouwer (2004) and many others pointed out that, the oil production from existing fields around the globe is reaching a plateau and the number of new significant discoveries per year is decreasing. This has made the oil and gas industry to rethink about its strategy for effective reservoir management in order to optimize production under given circumstances. Production optimization, which can provide a way to maximize the cumulative oil production through production and injection rate controls, can be deployed to answer some of these problems. In terms of efficient reservoir management, production optimization can be achieved through maximizing (e.g. NPV or cumulative oil production) or minimizing (e.g. cumulative water production or gas rate for flaring, etc.) a particular objective function g(x), also known as a cost function, depending upon the ultimate goals. Here, x can be thought of as a set of control variables that may include injection and production rates and bottom-hole pressures which are to be varied with production time intelligently in order to get the optimum results. As the relationship between the reservoir dynamics and the control variables is often highly non-linear, finding the right set of control variables to get optimum solution is a challenging task. In addition, the control variables are subject to operational constraints, such as the minimum well bottom-hole flow pressure, maximum water injection rates, and surface facility handling capacities, etc., which makes it a constrained optimization problem.