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Handling risk of uncertainty in model-based production optimization: a robust hierarchical approach

机译:在基于模型的生产优化中处理不确定性风险:稳健的分层方法

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Model-based economic optimization of oil production suffers from high levels of uncertainty. The limited knowledge of reservoir model parameters and varying economic conditions are the main contributors of uncertainty. The negative impact of these uncertainties on production strategy increases and becomes profound with time. In this work, a multi-objective optimization problem is formulated which considers both economic and model uncertainties and aims to mitigate the negative effects i.e., risk of these uncertainties on the production strategy. The improved robustness is achieved without heavily compromising the primary objective of economic life-cycle performance. An ensemble of varying oil price scenarios and geological model realizations are used to characterize the economic and geological uncertainty space respectively. The primary objective is an average NPV over these ensembles. As the risk of uncertainty increases with time, the secondary objective is aimed at maximizing the speed of oil production to mitigate risk. This multi-objective optimization is implemented separately with both forms of uncertainty in a hierarchical or lexicographic way.
机译:石油生产的基于模型的经济优化面临高度不确定性。储层模型参数的知识有限以及经济条件的变化是不确定性的主要因素。这些不确定性对生产策略的负面影响随着时间的流逝而增加并变得深远。在这项工作中,提出了一个多目标优化问题,该问题考虑了经济和模型的不确定性,旨在减轻负面影响,即这些不确定性对生产策略的风险。在不严重损害经济生命周期性能的主要目标的情况下,可以实现更高的鲁棒性。各种油价情景和地质模型实现的集合分别用于表征经济和地质不确定性空间。主要目标是这些集合的平均NPV。随着不确定性风险随时间增加,第二个目标旨在最大程度提高石油生产速度以减轻风险。此多目标优化以分层或词典方式分别使用两种形式的不确定性来实现。

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