Improved decision making with new efficient workflows for well placement optimization

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Determining optimum infill well placement has been one of most challenging events in overall field development strategy of any types of field. Because there could be a large number of possible candidates for new infill well locations, it is not practically feasible to evaluate all candidate locations, particularly at high-resolution geological models including millions of geological cells. Conventional static measure maps generally used in the industry has limited applicability as this does not address dynamic fluid flow and interference between existing wells. In contrast, direct application of stochastic search optimization methods such as genetic algorithms to large-scale field models may better account for dynamically changing reservoir conditions but can be complex to apply or computationally expensive.We propose a novel method for infill well placement optimization designed as a two-stage optimization based on both dynamic flow diagnostic characteristics and genetic algorithm optimization methods. The main advantage of the proposed method over the conventional approach of using static reservoir property maps is its ability to integrate static reservoir properties with dynamic variables such as sweep efficiencies. By incorporating sweep efficiencies, new infill well potential map is generated to guide the optimization. The other benefits of this method are its maneuverability and capability to search in a narrower and tight space making the evolutionary algorithm an appropriate choice to identify rewarding locations which was not practically applicable due to the significant computational burden.The proposed automated workflow is seamlessly integrated from building a simulation model to performing reservoir simulation and conducting the optimization process. The workflow is combined with novel engineering methods to identify best rewarding well location areas, and to identify strategies to maximize sweep efficiency through optimizing well placement and completion strategies resulting in substantial engineering time-savings. The power and utility of the proposed workflow are first demonstrated by a synthetic example and then applied to a full field model. Strategies to improve incremental oil recoveries were identified with a proposed novel, iterative and robust workflow.