The exploitation of subsurface hydrocarbon reservoirs is achieved through the control of production and injection wells (i.e., by prescribing time-varying pressures and flow rates) to create conditions that make the hydrocarbons trapped in the pores of the rock formation flow to the surface. The design of production strategies to exploit these reservoirs in the most efficient way requires an optimization framework that reflects the nature of the operational decisions and geological uncertainties involved. This paper introduces a new approach for production optimization in the context of closed-loop reservoir management (CLRM) by considering the impact of future measurements within the optimization framework. CLRM enables instrumented oil fields to be operated more efficiently through the systematic use of life-cycle production optimization and computer-assisted history matching. Recently, we have proposed a methodology to assess the value of information (VOI) of measurements in such a CLRM approach a-priori, i.e. during the field development planning phase, to improve the planned history matching component of CLRM. The reasoning behind the a-priori VOI analysis unveils an opportunity to also improve our approach to the production optimization problem by anticipating the fact that additional information (e.g., production measurements) will become available in the future. Here, we show how the more conventional optimization approach can be combined with VOI considerations to come up with a novel workflow, which we refer to as informed production optimization. We illustrate the concept with a simple water flooding problem in a two-dimensional five-spot reservoir and the results obtained confirm that this new approach can lead to significantly better decisions in some cases.

中文翻译：

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油气藏的知情生产优化

地下油气藏的开采是通过控制生产井和注入井（即规定时变压力和流速）来创造条件的，从而使困在岩层孔隙中的碳氢化合物流到地面。为了以最有效的方式开采这些油藏而设计的生产策略需要一个优化框架，该框架能够反映出业务决策的性质和所涉及的地质不确定性。本文通过考虑优化框架内未来测量的影响，介绍了一种在闭环油藏管理（CLRM）环境下进行生产优化的新方法。CLRM通过系统地使用生命周期生产优化和计算机辅助的历史记录匹配，可以使仪器仪表的油田更有效地运行。最近，我们提出了一种方法来评估这种CLRM方法的先验性（即在田间开发计划阶段）测量值的信息（VOI），以改善CLRM的计划历史匹配组件。先验VOI分析背后的理由揭示了一个机会，即通过预期将来会提供更多信息（例如，生产度量）这一事实，也可以改善我们对生产优化问题的方法。在这里，我们展示了如何将更常规的优化方法与VOI考虑因素结合起来以提出新颖的工作流程，我们将其称为明智的生产优化。我们用二维五点水库中的简单水驱问题说明了这一概念，所获得的结果证实了这种新方法在某些情况下可以导致更好的决策。