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Optimal Rate Control Under Geologic Uncertainty

机译:地质不确定性下的最佳速率控制

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Waterflood optimization via rate control is receiving increased interest because of rapid developments in the smart well completions and i-field technology. The use of inflow control valves (ICV) allows us to optimize the production/injection rates of various segments along the wellbore, thereby maximizing sweep efficiency and delaying water breakthrough. A major challenge for practical field implementation of this technology is dealing with geologic uncertainty. In practice, the reservoir geology is known only in a probabilistic sense;hence, the optimization of smart wells should be carried out in a stochastic framework to account for geologic uncertainty. We propose a practical and efficient approach for computing optimal injection and production rates accounting for geological uncertainty. The approach relies on equalizing arrival time of the waterfront at all producers using multiple geologic realizations. The main objective is to improve sweep efficiency and thereby improve oil production and recovery. We account for geologic uncertainty using two optimization schemes. The first one is to formulate the objective function in a stochastic form which relies on a combination of expected value and standard deviation combined with a risk attitude coefficient. The second one is to minimize the worst case scenario using a min-max problem formulation. The optimization is performed under operational and facility constraints using a sequential quadratic programming approach. A major advantage of our approach is the analytical computation of the gradient and Hessian of the objective function which makes it computationally efficient and suitable for large field cases. Multiple examples are presented to support the robustness and efficiency of the proposed optimization scheme. These include 2D synthetic examples for validation and a 3D field-scale application. The role of geologic uncertainty in the outcome of the optimization is demonstrated both during the early stage and also, later stages of waterflooding when substantial production history is available.
机译:由于智能井完井和I场技术的快速发展,通过速率控制通过速率控制正在接受增加的兴趣。流入控制阀(ICV)的使用使我们能够优化沿井筒的各种区段的生产/注射速率,从而最大化扫描效率和延迟水突破。这种技术实际实施的一项重大挑战是处理地质不确定性。在实践中,储层地质只在概率意义上知道;因此,应在随机框架中进行智能井的优化,以考虑地质不确定性。我们提出了一种实用而有效的方法,用于计算地质不确定性的最佳注射和生产率。该方法依赖于使用多种地质识别的所有生产者均衡的江边的到达时间。主要目的是提高扫描效率,从而改善石油生产和恢复。我们考虑了使用两个优化方案的地质不确定性。第一个是以随机形式制定目标函数,其依赖于预期值和标准偏差的组合与风险姿态系数相结合。第二个是使用MIN-MAX问题的制定来最小化最坏情况。使用顺序二次编程方法在操作和设施约束下执行优化。我们的方法的主要优点是客观函数的梯度和Hessian的分析计算,使其使其计算效率高,适用于大现场案例。提出了多个示例以支持所提出的优化方案的鲁棒性和效率。这些包括用于验证的2D合成示例和3D现场缩放应用程序。地质不确定性在优化的结果中的作用在早期的阶段和后来的水上阶段获得了实质性生产历史。

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