短期注水再分配STIR:使用先进数据分析的实时注水优化技术

Gaurav Modi, Manu Ujjwal, Srungeer Simha
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引用次数: 1

摘要

短期注水再分配(STIR)是一种基于python的棕地资产实时注水优化技术,使用先进的数据分析技术。该技术的目的是为注入水的重新分配提供建议,以最大限度地提高设施的产油量。尽管这是一种数据驱动的技术,但它受到石油工程原理(如物质平衡等)的严格限制。该工作流程集成并分析了油藏、油井和设施层面的短期数据(过去3-6个月)。STIR工作流程分为三个模块:注采器连通性注入效率注水优化第一个模块使用四种主要数据类型来估计油藏中每个注采器对之间的连通性:生产数据(压力、WC、GOR、盐度)、断层存在情况、地下距离、射孔相似度、层数和kh;第二个模块使用连通性和含水数据来确定注入效率。效率高的注入器对生产的贡献最大,而效率低的注入器对水循环的贡献最大。第三个模块有一个数学优化器,通过在生产系统的每个节点(井、设施等)的约束下,通过在注入器之间重新分配注水来最大限度地提高石油产量。STIR工作流程已应用于不同资产的6个油藏,预计年产油量将增加3-7%。每个建议都使用独立的数据源进行验证,因此,生成的建议与油藏的了解非常一致。在3-6个月的实施过程中,可以看到该技术在提高产油量和更好地支持低含水生产商(增加压力)方面的优势。工作流程固有的灵活性允许在任何水淹油藏中轻松复制,并且当油藏中的注入井数量相对较高时效果最佳。地质特征在工作流中很好地表示,这是该技术的独特功能之一。该方法还为生产商提供了增产和注入器增产的机会。这种低成本(无资本支出)的技术具有传统石油工程技术和数据驱动方法的优势。该技术为棕地的水驱管理提供了很好的替代方案,在棕地,进行可靠的常规分析是具有挑战性的,有时甚至是不可能的。STIR可以在3-6周的时间内在油藏中实现。
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Short Term Injection Re-Distribution STIR: Real-Time Waterflood Optimization Technique Using Advanced Data Analytics
Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level. STIR workflow is divided into three modules: Injector-producer connectivity Injector efficiency Injection water optimization First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir: Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling. Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system. The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities. This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.
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