以模型为基础优化碳酸氢盐储层中对生成器、喷射器和 WAG-CO2 循环大小的 ICV 控制

V. Botechia, D. J. Schiozer
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引用次数: 0

摘要

油田的生命周期管理涉及复杂的决策,需要考虑许多变量。在巴西前盐层的一些油田,由于地处超深水区,天然气(包括二氧化碳)产量巨大,因此这项任务更具挑战性。这一特点成为这类油田石油生产的一个限制因素,而适当的管理对于克服这一问题、提高产量和经济收益至关重要。因此,水-替代气(WAG)经常被用作一种提高石油采收率的机制,可以提供更好的流动性控制,提高储层扫描和石油采收率。此外,WAG 还可以作为回收利用产出气体的替代方法,提供适当的去向(主要是 CO2),避免环境问题。在这种情况下,我们将这种方法命名为 WAG-CO2。此外,间歇控制阀(ICV)也是处理这些气田的一个很好的替代方法,因为它们可以作为流量控制装置,更好地平衡储层中的流体流动。在这项工作中,我们评估了在生产商、注入商中使用间歇控制阀(ICV)的影响,以及 WAG 循环尺寸对改善已开发油田性能的影响。这项工作由两部分组成。在第一部分中,我们使用单一模拟模型,执行了(1)六种分层优化方法,以验证优化顺序是否会影响最终结果,以及(2)所有参数的联合优化,以进行比较。我们在一个参考案例(代表实地的 "真实反应")中模拟了最终策略,以评估所实现的控制规则在真实实地中的表现。在第二部分中,我们考虑了一种概率程序,将其中一种分层方法应用于表达不确定情况的几个代表性模型(RM)中。在这种情况下,我们将单个 RM 的名义优化与稳健优化(同时优化所有 RM)进行了比较。研究应用于 SEC1_2022 基准案例,这是一个类似于前盐田的合成模型。结果表明,尽管平台受到天然气生产能力的限制,但通过适当运行 ICV 和 WAG-CO2 循环的大小,油气田的经济回报率有了明显提高。出现这一结果的原因是,流经储层和各井的流体流量更加平衡。此外,在工作的第一部分,所有分层方法的净现值结果都非常相似,因此优化步骤的顺序对结果没有显著影响。非分层方法的结果稍好,但仍与分层方法的结果接近。此外,影响最大的变量是生产商的 ICV,其次是喷射器的 ICV,然后是 WAG-CO2 循环。在工作的第二部分也观察到类似的趋势,稳健优化的预期经济回报更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MODEL-BASED OPTIMIZATION OF ICVs’ CONTROL FOR PRODUCERS, INJECTORS, AND WAG-CO2 CYCLES SIZE IN A CARBONATE RESERVOIR UNDER UNCERTAINTY
Life cycle management of oil fields involves complex decisions, with many variables to be considered. In some fields of the Brazilian pre-salt, this task is even more challenging given the ultra-deep water location, with an expressive amount of gas (including CO2) production. This feature becomes a restricting factor for oil production in these types of fields, and a proper management is essential to overcome this issue, improving both production and economic returns. Therefore, Water-Alternate-Gas (WAG) is often used as an Enhanced Oil Recovery- EOR mechanism, providing a better mobility control, improving reservoir sweep and oil recovery. Moreover, WAG can be an alternative for recycling the produced gas, providing an adequate destination (mainly for CO2) and avoiding environmental problems. In this case, we name the method as WAG-CO2. Moreover, Interval Control Valves (ICVs) are also a good alternative to deal with these fields, since they work as flow control devices and allow for a better balance of the fluid flow in the reservoir. In this work, we evaluate the impact of the use of ICVs in producers, injectors, along with WAG cycles size to improve the performance of a developed field. The work consists of two parts. In Part I, using a single simulation model, we performed (1) six hierarchical optimization approaches to verify if the order in which the optimization occurs influences the final results, and (2) a joint optimization of all parameters for comparison purposes. The final strategies are simulated in a reference case (representing the “true response” of the field) to evaluate how the achieved control rules would behave in a real field. In Part II, we considered a probabilistic procedure, applying one of the hierarchical approaches in several representative models (RMs) expressing uncertain scenarios. In this case, we compared nominal optimization of individual RMs against a robust optimization (in which all RMs are optimized simultaneously). The study is applied to the benchmark case SEC1_2022, a synthetic model analogous to a pre-salt field. The results show an expressive increase in the economic return of the field with a proper operation of the ICVs and size of the WAG-CO2 cycles, despite the platform being restricted by gas production capacity. This result occurred because of a better balance in fluid flow through the reservoir and among wells. Moreover, in the first part of the work, all hierarchical approaches presented very similar results in terms of NPV, so the order of the optimization steps did not significantly affect the results. The non-hierarchical approach presented a slightly better result, but still close to those obtained in the hierarchical approaches. Furthermore, the most impacting variable was the ICV for producers, followed by ICV for injectors and, then, WAG-CO2 cycles. Similar tendencies were observed in the second part of the work, with higher expected economic return for the robust optimization.
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