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Optimizing Development Strategy and Maximizing Field Economic Recovery through Simulation Opportunity Index

机译:通过模拟机会指数优化发展战略和最大化现场经济复苏

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With the rapidly increasing costs of field development, one of the many challenges for any green or brown field development is to optimize the number and locations of development wells that would yield maximum sweep efficiency and recovery. A full field simulation model is generally constructed to assist in optimizing the development plans and in many occasions initial well planning is based on fixed well length, well orientation, well spacing, etc., before any iterative sensitivity runs are performed. It is recognized that in most cases, the decision was heavily influenced by a single reservoir property, such as permeability, without considering the effect of other inter-related parameters, such as movable oil volume, fluid viscosity, hydrocarbon pore volume, reservoir permeability-thickness and residual oil saturation. The need to analyze these variables in a structured and logical manner is therefore a challenge to ensure a technically sound decision making process. In this paper a modified approach, based on techniques already established (Molina et al., 2009), hereinafter called “Simulation Opportunity Index” (SOI) is introduced. It provides an innovative view on how a development strategy is addressed, and subsequently forms the basis of well placement optimization. All steps and equations to generate the desired indexes are discussed in this paper. We have successfully utilized industrial geological modeling software to perform the calculations and eventually generate the three main index properties in a 3D reservoir model. Using an in-house well planning tool, we can place new wells to optimize field development. This paper also presents a case study for a major oil field development in Saudi Arabia. Comparison between conventional approach and this modified technique clearly showed an improved field recovery performance.
机译:随着现场发展成本的迅速增加,任何绿色或棕色现场开发的许多挑战之一就是优化发展井的数量和位置,这将产生最大的扫描效率和恢复。通常构建全场仿真模型以帮助优化开发计划,并且在许多场合,初始井规划基于在执行任何迭代灵敏度运行之前基于固定井长度,井取向,间距等。据认识到,在大多数情况下,该决定受到单一储层性质的严重影响,例如渗透性,而不考虑其他相关参数的效果,例如可移动油量,流体粘度,烃孔体积,储层渗透性 - 厚度和残留的油饱和度。因此,需要以结构化和逻辑方式分析这些变量是一种挑战,以确保技术良好的决策过程。在本文中,基于已经建立的技术(Molina等,2009)的修改方法,下文中称为“模拟机会指数”(SOI)。它为如何解决开发战略提供了创新的观点,并随后构成了井放置优化的基础。本文讨论了生成所需索引的所有步骤和方程。我们已成功利用工业地质建模软件来执行计算,最终在3D储液模型中产生三个主要指标属性。使用内部井规划工具,我们可以放置新的井来优化现场开发。本文还为沙特阿拉伯的主要油田开发提供了一个案例研究。传统方法与该修改技术之间的比较清楚地显示了改进的现场恢复性能。

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