As many fields around the world are reaching maturity, the need to develop new tools that allow reservoir engineers to optimize reservoir performance is becoming more urgent. One of the more challenging and important problems along these lines is the well placement optimization problem. In this problem, there are many variables to consider: geological variables like reservoir architecture, permeability and porosity distributions, and fluid contacts; production variables, such as well placement, well number, well type, and production rate; and economic variables like fluid prices and drilling costs. Furthermore, availability of complex well types, such as multilateral wells (MLWs) and maximum reservoir contact (MRC) wells, aggravate this challenge. All these variables, together with reservoir geological uncertainty, make the determination of an optimum development plan for a given field difficult.

The objective of this work was to employ an optimization technique that can efficiently address the aforementioned challenges. Based on the success and versatility of Genetic Algorithms (GAs) in problems of high complexity with high dimensionality and nonlinearity, it is used here as the main optimization engine. Both binary GA (bGA) and continuous GA (cGA) were tested in the optimization of well location and design in terms of well type, number of laterals, and well and lateral trajectories in a channelized synthetic model. Both GA variants showed significant improvement over initial solutions but comparisons between the two types showed that the cGA was more robust for the problem under consideration. The cGA was, thereafter, applied to a real field located in the Middle East to investigate its robustness in optimizing well location and design in more complex reservoir models. The model is an upscaled version for an offshore carbonate reservoir, which is mildly heterogeneous with low and high permeability areas scattered over the field.

After choosing the optimization technique to achieve our objective, considerable work was performed to study the sensitivity of the different algorithm parameters on converged solutions. Then, multiple optimization runs were performed to obtain a sound development plan for this field. An attempt was made to quantify how solutions were affected by some of the assumptions and preconditioning steps taken during optimization. Finally, an optimization ran was performed on the fine model using optimized solutions from the coarse model.

Results showed that the optimum well configuration for the reservoir model at hand can contain five or more laterals; which shows potential for drilling MRC wells. Other studies comparing results from the fine and coarse reservoir models revealed that the best solutions are different between the two models. In general, solutions from different runs had different well designs due to the stochastic nature of the algorithm but some guidance about preferred well locations could be obtained through this process

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